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Science Policy Forum: Should we trust healthcare explanations from AI predictive systems?
Some in industry voice their concerns
Curator: Stephen J. Williams, PhD
Post on AI healthcare and explainable AI
In a Policy Forum article in Science “Beware explanations from AI in health care”, Boris Babic, Sara Gerke, Theodoros Evgeniou, and Glenn Cohen discuss the caveats on relying on explainable versus interpretable artificial intelligence (AI) and Machine Learning (ML) algorithms to make complex health decisions. The FDA has already approved some AI/ML algorithms for analysis of medical images for diagnostic purposes. These have been discussed in prior posts on this site, as well as issues arising from multi-center trials. The authors of this perspective article argue that choice of type of algorithm (explainable versus interpretable) algorithms may have far reaching consequences in health care.
Summary
Artificial intelligence and machine learning (AI/ML) algorithms are increasingly developed in health care for diagnosis and treatment of a variety of medical conditions (1). However, despite the technical prowess of such systems, their adoption has been challenging, and whether and how much they will actually improve health care remains to be seen. A central reason for this is that the effectiveness of AI/ML-based medical devices depends largely on the behavioral characteristics of its users, who, for example, are often vulnerable to well-documented biases or algorithmic aversion (2). Many stakeholders increasingly identify the so-called black-box nature of predictive algorithms as the core source of users’ skepticism, lack of trust, and slow uptake (3, 4). As a result, lawmakers have been moving in the direction of requiring the availability of explanations for black-box algorithmic decisions (5). Indeed, a near-consensus is emerging in favor of explainable AI/ML among academics, governments, and civil society groups. Many are drawn to this approach to harness the accuracy benefits of noninterpretable AI/ML such as deep learning or neural nets while also supporting transparency, trust, and adoption. We argue that this consensus, at least as applied to health care, both overstates the benefits and undercounts the drawbacks of requiring black-box algorithms to be explainable.
Types of AI/ML Algorithms: Explainable and Interpretable algorithms
Interpretable AI: A typical AI/ML task requires constructing algorithms from vector inputs and generating an output related to an outcome (like diagnosing a cardiac event from an image). Generally the algorithm has to be trained on past data with known parameters. When an algorithm is called interpretable, this means that the algorithm uses a transparent or “white box” function which is easily understandable. Such example might be a linear function to determine relationships where parameters are simple and not complex. Although they may not be as accurate as the more complex explainable AI/ML algorithms, they are open, transparent, and easily understood by the operators.
Explainable AI/ML: This type of algorithm depends upon multiple complex parameters and takes a first round of predictions from a “black box” model then uses a second algorithm from an interpretable function to better approximate outputs of the first model. The first algorithm is trained not with original data but based on predictions resembling multiple iterations of computing. Therefore this method is more accurate or deemed more reliable in prediction however is very complex and is not easily understandable. Many medical devices that use an AI/ML algorithm use this type. An example is deep learning and neural networks.
The purpose of both these methodologies is to deal with problems of opacity, or that AI predictions based from a black box undermines trust in the AI.
For a deeper understanding of these two types of algorithms see here:
How interpretability is different from explainability
Why a model might need to be interpretable and/or explainable
Who is working to solve the black box problem—and how
What is interpretability?
Does Chipotle make your stomach hurt? Does loud noise accelerate hearing loss? Are women less aggressive than men? If a machine learning model can create a definition around these relationships, it is interpretable.
All models must start with a hypothesis. Human curiosity propels a being to intuit that one thing relates to another. “Hmm…multiple black people shot by policemen…seemingly out of proportion to other races…something might be systemic?” Explore.
People create internal models to interpret their surroundings. In the field of machine learning, these models can be tested and verified as either accurate or inaccurate representations of the world.
Interpretability means that the cause and effect can be determined.
What is explainability?
ML models are often called black-box models because they allow a pre-set number of empty parameters, or nodes, to be assigned values by the machine learning algorithm. Specifically, the back-propagation step is responsible for updating the weights based on its error function.
To predict when a person might die—the fun gamble one might play when calculating a life insurance premium, and the strange bet a person makes against their own life when purchasing a life insurance package—a model will take in its inputs, and output a percent chance the given person has at living to age 80.
Below is an image of a neural network. The inputs are the yellow; the outputs are the orange. Like a rubric to an overall grade, explainability shows how significant each of the parameters, all the blue nodes, contribute to the final decision.
In this neural network, the hidden layers (the two columns of blue dots) would be the black box.
For example, we have these data inputs:
Age
BMI score
Number of years spent smoking
Career category
If this model had high explainability, we’d be able to say, for instance:
The career category is about 40% important
The number of years spent smoking weighs in at 35% important
The age is 15% important
The BMI score is 10% important
Explainability: important, not always necessary
Explainability becomes significant in the field of machine learning because, often, it is not apparent. Explainability is often unnecessary. A machine learning engineer can build a model without ever having considered the model’s explainability. It is an extra step in the building process—like wearing a seat belt while driving a car. It is unnecessary for the car to perform, but offers insurance when things crash.
The benefit a deep neural net offers to engineers is it creates a black box of parameters, like fake additional data points, that allow a model to base its decisions against. These fake data points go unknown to the engineer. The black box, or hidden layers, allow a model to make associations among the given data points to predict better results. For example, if we are deciding how long someone might have to live, and we use career data as an input, it is possible the model sorts the careers into high- and low-risk career options all on its own.
Perhaps we inspect a node and see it relates oil rig workers, underwater welders, and boat cooks to each other. It is possible the neural net makes connections between the lifespan of these individuals and puts a placeholder in the deep net to associate these. If we were to examine the individual nodes in the black box, we could note this clustering interprets water careers to be a high-risk job.
In the previous chart, each one of the lines connecting from the yellow dot to the blue dot can represent a signal, weighing the importance of that node in determining the overall score of the output.
If that signal is high, that node is significant to the model’s overall performance.
If that signal is low, the node is insignificant.
With this understanding, we can define explainability as:
Knowledge of what one node represents and how important it is to the model’s performance.
So how does choice of these two different algorithms make a difference with respect to health care and medical decision making?
The authors argue:
“Regulators like the FDA should focus on those aspects of the AI/ML system that directly bear on its safety and effectiveness – in particular, how does it perform in the hands of its intended users?”
A suggestion for
Enhanced more involved clinical trials
Provide individuals added flexibility when interacting with a model, for example inputting their own test data
More interaction between user and model generators
Determining in which situations call for interpretable AI versus explainable (for instance predicting which patients will require dialysis after kidney damage)
Other articles on AI/ML in medicine and healthcare on this Open Access Journal include
Improving diagnostic yield in pediatric cancer precision medicine
Elaine R Mardis
Advent of genomics have revolutionized how we diagnose and treat lung cancer
We are currently needing to understand the driver mutations and variants where we can personalize therapy
PD-L1 and other checkpoint therapy have not really been used in pediatric cancers even though CAR-T have been successful
The incidence rates and mortality rates of pediatric cancers are rising
Large scale study of over 700 pediatric cancers show cancers driven by epigenetic drivers or fusion proteins. Need for transcriptomics. Also study demonstrated that we have underestimated germ line mutations and hereditary factors.
They put together a database to nominate patients on their IGM Cancer protocol. Involves genetic counseling and obtaining germ line samples to determine hereditary factors. RNA and protein are evaluated as well as exome sequencing. RNASeq and Archer Dx test to identify driver fusions
PECAN curated database from St. Jude used to determine driver mutations. They use multiple databases and overlap within these databases and knowledge base to determine or weed out false positives
They have used these studies to understand the immune infiltrate into recurrent cancers (CytoCure)
They found 40 germline cancer predisposition genes, 47 driver somatic fusion proteins, 81 potential actionable targets, 106 CNV, 196 meaningful somatic driver mutations
They are functioning well at NCI with respect to grant reviews, research, and general functions in spite of the COVID pandemic and the massive demonstrations on also focusing on the disparities which occur in cancer research field and cancer care
There are ongoing efforts at NCI to make a positive difference in racial injustice, diversity in the cancer workforce, and for patients as well
Need a diverse workforce across the cancer research and care spectrum
Data show that areas where the clinicians are successful in putting African Americans on clinical trials are areas (geographic and site specific) where health disparities are narrowing
Grants through NCI new SeroNet for COVID-19 serologic testing funded by two RFAs through NIAD (RFA-CA-30-038 and RFA-CA-20-039) and will close on July 22, 2020
Tuesday, June 23
12:45 PM – 1:46 PM EDT
Virtual Educational Session
Immunology, Tumor Biology, Experimental and Molecular Therapeutics, Molecular and Cellular Biology/Genetics
This educational session will update cancer researchers and clinicians about the latest developments in the detailed understanding of the types and roles of immune cells in tumors. It will summarize current knowledge about the types of T cells, natural killer cells, B cells, and myeloid cells in tumors and discuss current knowledge about the roles these cells play in the antitumor immune response. The session will feature some of the most promising up-and-coming cancer immunologists who will inform about their latest strategies to harness the immune system to promote more effective therapies.
Judith A Varner, Yuliya Pylayeva-Gupta
Introduction
Judith A Varner
New techniques reveal critical roles of myeloid cells in tumor development and progression
Different type of cells are becoming targets for immune checkpoint like myeloid cells
In T cell excluded or desert tumors T cells are held at periphery so myeloid cells can infiltrate though so macrophages might be effective in these immune t cell naïve tumors, macrophages are most abundant types of immune cells in tumors
CXCLs are potential targets
PI3K delta inhibitors,
Reduce the infiltrate of myeloid tumor suppressor cells like macrophages
When should we give myeloid or T cell therapy is the issue
Judith A Varner
Novel strategies to harness T-cell biology for cancer therapy
Positive and negative roles of B cells in cancer
Yuliya Pylayeva-Gupta
New approaches in cancer immunotherapy: Programming bacteria to induce systemic antitumor immunity
There are numerous examples of highly successful covalent drugs such as aspirin and penicillin that have been in use for a long period of time. Despite historical success, there was a period of reluctance among many to purse covalent drugs based on concerns about toxicity. With advances in understanding features of a well-designed covalent drug, new techniques to discover and characterize covalent inhibitors, and clinical success of new covalent cancer drugs in recent years, there is renewed interest in covalent compounds. This session will provide a broad look at covalent probe compounds and drug development, including a historical perspective, examination of warheads and electrophilic amino acids, the role of chemoproteomics, and case studies.
Benjamin F Cravatt, Richard A. Ward, Sara J Buhrlage
Discovering and optimizing covalent small-molecule ligands by chemical proteomics
Benjamin F Cravatt
Multiple approaches are being investigated to find new covalent inhibitors such as: 1) cysteine reactivity mapping, 2) mapping cysteine ligandability, 3) and functional screening in phenotypic assays for electrophilic compounds
Using fluorescent activity probes in proteomic screens; have broad useability in the proteome but can be specific
They screened quiescent versus stimulated T cells to determine reactive cysteines in a phenotypic screen and analyzed by MS proteomics (cysteine reactivity profiling); can quantitate 15000 to 20,000 reactive cysteines
Isocitrate dehydrogenase 1 and adapter protein LCP-1 are two examples of changes in reactive cysteines they have seen using this method
They use scout molecules to target ligands or proteins with reactive cysteines
For phenotypic screens they first use a cytotoxic assay to screen out toxic compounds which just kill cells without causing T cell activation (like IL10 secretion)
INTERESTINGLY coupling these MS reactive cysteine screens with phenotypic screens you can find NONCANONICAL mechanisms of many of these target proteins (many of the compounds found targets which were not predicted or known)
Electrophilic warheads and nucleophilic amino acids: A chemical and computational perspective on covalent modifier
The covalent targeting of cysteine residues in drug discovery and its application to the discovery of Osimertinib
Richard A. Ward
Cysteine activation: thiolate form of cysteine is a strong nucleophile
Thiolate form preferred in polar environment
Activation can be assisted by neighboring residues; pKA will have an effect on deprotonation
pKas of cysteine vary in EGFR
cysteine that are too reactive give toxicity while not reactive enough are ineffective
Accelerating drug discovery with lysine-targeted covalent probes
This Educational Session aims to guide discussion on the heterogeneous cells and metabolism in the tumor microenvironment. It is now clear that the diversity of cells in tumors each require distinct metabolic programs to survive and proliferate. Tumors, however, are genetically programmed for high rates of metabolism and can present a metabolically hostile environment in which nutrient competition and hypoxia can limit antitumor immunity.
Jeffrey C Rathmell, Lydia Lynch, Mara H Sherman, Greg M Delgoffe
T-cell metabolism and metabolic reprogramming antitumor immunity
Jeffrey C Rathmell
Introduction
Jeffrey C Rathmell
Metabolic functions of cancer-associated fibroblasts
Mara H Sherman
Tumor microenvironment metabolism and its effects on antitumor immunity and immunotherapeutic response
Greg M Delgoffe
Multiple metabolites, reactive oxygen species within the tumor microenvironment; is there heterogeneity within the TME metabolome which can predict their ability to be immunosensitive
Took melanoma cells and looked at metabolism using Seahorse (glycolysis): and there was vast heterogeneity in melanoma tumor cells; some just do oxphos and no glycolytic metabolism (inverse Warburg)
As they profiled whole tumors they could separate out the metabolism of each cell type within the tumor and could look at T cells versus stromal CAFs or tumor cells and characterized cells as indolent or metabolic
T cells from hyerglycolytic tumors were fine but from high glycolysis the T cells were more indolent
When knock down glucose transporter the cells become more glycolytic
If patient had high oxidative metabolism had low PDL1 sensitivity
Showed this result in head and neck cancer as well
Metformin a complex 1 inhibitor which is not as toxic as most mito oxphos inhibitors the T cells have less hypoxia and can remodel the TME and stimulate the immune response
Metformin now in clinical trials
T cells though seem metabolically restricted; T cells that infiltrate tumors are low mitochondrial phosph cells
T cells from tumors have defective mitochondria or little respiratory capacity
They have some preliminary findings that metabolic inhibitors may help with CAR-T therapy
Obesity, lipids and suppression of anti-tumor immunity
Lydia Lynch
Hypothesis: obesity causes issues with anti tumor immunity
Less NK cells in obese people; also produce less IFN gamma
RNASeq on NOD mice; granzymes and perforins at top of list of obese downregulated
Upregulated genes that were upregulated involved in lipid metabolism
All were PPAR target genes
NK cells from obese patients takes up palmitate and this reduces their glycolysis but OXPHOS also reduced; they think increased FFA basically overloads mitochondria
Long recognized for their role in cancer diagnosis and prognostication, pathologists are beginning to leverage a variety of digital imaging technologies and computational tools to improve both clinical practice and cancer research. Remarkably, the emergence of artificial intelligence (AI) and machine learning algorithms for analyzing pathology specimens is poised to not only augment the resolution and accuracy of clinical diagnosis, but also fundamentally transform the role of the pathologist in cancer science and precision oncology. This session will discuss what pathologists are currently able to achieve with these new technologies, present their challenges and barriers, and overview their future possibilities in cancer diagnosis and research. The session will also include discussions of what is practical and doable in the clinic for diagnostic and clinical oncology in comparison to technologies and approaches primarily utilized to accelerate cancer research.
Jorge S Reis-Filho, Thomas J Fuchs, David L Rimm, Jayanta Debnath
Using old methods and new methods; so cell counting you use to find the cells then phenotype; with quantification like with Aqua use densitometry of positive signal to determine a threshold to determine presence of a cell for counting
Hiplex versus multiplex imaging where you have ten channels to measure by cycling of flour on antibody (can get up to 20plex)
Hiplex can be coupled with Mass spectrometry (Imaging Mass spectrometry, based on heavy metal tags on mAbs)
However it will still take a trained pathologist to define regions of interest or field of desired view
Introduction
Jayanta Debnath
Challenges and barriers of implementing AI tools for cancer diagnostics
Jorge S Reis-Filho
Implementing robust digital pathology workflows into clinical practice and cancer research
Jayanta Debnath
Invited Speaker
Thomas J Fuchs
Founder of spinout of Memorial Sloan Kettering
Separates AI from computational algothimic
Dealing with not just machines but integrating human intelligence
Making decision for the patients must involve human decision making as well
How do we get experts to do these decisions faster
AI in pathology: what is difficult? =è sandbox scenarios where machines are great,; curated datasets; human decision support systems or maps; or try to predict nature
1) learn rules made by humans; human to human scenario 2)constrained nature 3)unconstrained nature like images and or behavior 4) predict nature response to nature response to itself
In sandbox scenario the rules are set in stone and machines are great like chess playing
In second scenario can train computer to predict what a human would predict
So third scenario is like driving cars
System on constrained nature or constrained dataset will take a long time for commuter to get to decision
Fourth category is long term data collection project
He is finding it is still finding it is still is difficult to predict nature so going from clinical finding to prognosis still does not have good predictability with AI alone; need for human involvement
End to end partnering (EPL) is a new way where humans can get more involved with the algorithm and assist with the problem of constrained data
An example of a workflow for pathology would be as follows from Campanella et al 2019 Nature Medicine: obtain digital images (they digitized a million slides), train a massive data set with highthroughput computing (needed a lot of time and big software developing effort), and then train it using input be the best expert pathologists (nature to human and unconstrained because no data curation done)
Led to first clinically grade machine learning system (Camelyon16 was the challenge for detecting metastatic cells in lymph tissue; tested on 12,000 patients from 45 countries)
The first big hurdle was moving from manually annotated slides (which was a big bottleneck) to automatically extracted data from path reports).
Now problem is in prediction: How can we bridge the gap from predicting humans to predicting nature?
With an AI system pathologist drastically improved the ability to detect very small lesions
Incidence rates of several cancers (e.g., colorectal, pancreatic, and breast cancers) are rising in younger populations, which contrasts with either declining or more slowly rising incidence in older populations. Early-onset cancers are also more aggressive and have different tumor characteristics than those in older populations. Evidence on risk factors and contributors to early-onset cancers is emerging. In this Educational Session, the trends and burden, potential causes, risk factors, and tumor characteristics of early-onset cancers will be covered. Presenters will focus on colorectal and breast cancer, which are among the most common causes of cancer deaths in younger people. Potential mechanisms of early-onset cancers and racial/ethnic differences will also be discussed.
Stacey A. Fedewa, Xavier Llor, Pepper Jo Schedin, Yin Cao
Cancers that are and are not increasing in younger populations
Stacey A. Fedewa
Early onset cancers, pediatric cancers and colon cancers are increasing in younger adults
Younger people are more likely to be uninsured and these are there most productive years so it is a horrible life event for a young adult to be diagnosed with cancer. They will have more financial hardship and most (70%) of the young adults with cancer have had financial difficulties. It is very hard for women as they are on their childbearing years so additional stress
Types of early onset cancer varies by age as well as geographic locations. For example in 20s thyroid cancer is more common but in 30s it is breast cancer. Colorectal and testicular most common in US.
SCC is decreasing by adenocarcinoma of the cervix is increasing in women’s 40s, potentially due to changing sexual behaviors
Breast cancer is increasing in younger women: maybe etiologic distinct like triple negative and larger racial disparities in younger African American women
Increased obesity among younger people is becoming a factor in this increasing incidence of early onset cancers
Other Articles on this Open Access Online Journal on Cancer Conferences and Conference Coverage in Real Time Include
2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET
Dialogue among principals is a World Forum’s signature. Expert moderators guiding discussion and questions in audience friendly exchanges. No slides – shared perspectives facilitated by Harvard faculty, leading journalists and Mass General Brigham executives.
Jeffrey Golden, MD
Chair, Department of Pathology, BH; Ramzi S. Cotran Professor of Pathology, Harvard Medical School
Hadine Joffe, MD
Vice Chair, Psychiatry, Executive Director, Mary Horrigan Connors Center for Women’s Health and Gender Biology, BH; Paula A. Johnson Professor, Women’s Health, Harvard Medical School
Thomas Sequist, MD
Chief Patient Experience and Equity Officer, Mass General Brigham; Professor of Medicine and Health Care Policy, Harvard Medical School
Erica Shenoy, MD, PhD
Associate Chief, Infection Control Unit, MGH; Assistant Professor, Harvard Medical School
Gregg Meyer, MD
Chief Clinical Officer, Mass General Brigham; Interim President, NWH; Professor, Harvard Medical School
Ravi Thadhani, MD
CAO, Mass General Brigham; Professor and Faculty Dean for Academic Programs, Harvard Medical School
Ann Prestipino
SVP; Incident Commander, MGH
Roger Kitterman
VP, Venture and Managing Partner, Partners Innovation Fund, Mass General Brigham
David Louis, MD
Pathologist-in-Chief, MGH; Benjamin Castleman Professor of Pathology, Harvard Medical School
Janet Wu
Bloomberg
Ron Walls, MD
EVP and Chief Operating Officer, BH; Neskey Family Professor of Emergency Medicine, Harvard Medical School
Alice Park
Senior Writer, TIME
Jeffrey Golden, MD
Chair, Department of Pathology, BH; Ramzi S. Cotran Professor of Pathology, Harvard Medical School
Hadine Joffe, MD
Vice Chair, Psychiatry, Executive Director, Mary Horrigan Connors Center for Women’s Health and Gender Biology, BH; Paula A. Johnson Professor, Women’s Health, Harvard Medical School
Thomas Sequist, MD
Chief Patient Experience and Equity Officer, Mass General Brigham; Professor of Medicine and Health Care Policy, Harvard Medical School
Erica Shenoy, MD, PhD
Associate Chief, Infection Control Unit, MGH; Assistant Professor, Harvard Medical School
Gregg Meyer, MD
Chief Clinical Officer, Mass General Brigham; Interim President, NWH; Professor, Harvard Medical School
Ravi Thadhani, MD
CAO, Mass General Brigham; Professor and Faculty Dean for Academic Programs, Harvard Medical School
Ann Prestipino
SVP; Incident Commander, MGH
Roger Kitterman
VP, Venture and Managing Partner, Partners Innovation Fund, Mass General Brigham
David Louis, MD
Pathologist-in-Chief, MGH; Benjamin Castleman Professor of Pathology, Harvard Medical School
Janet Wu
Bloomberg
Ron Walls, MD
EVP and Chief Operating Officer, BH; Neskey Family Professor of Emergency Medicine, Harvard Medical School
Subject: REGISTRANT RECAP | World Medical Innovation Forum
Dear World Forum Attendee,
On behalf of Mass General Brigham CEO Anne Klibanski MD and Forum co-Chairs Gregg Meyer MD and Ravi Thadhani MD, many thanks for being among the nearly 11,000 registrants representing 93 countries, 46 states and 3200 organizations yesterday. A community was established around many pressing topics that will continue long into the future. We hope you have a chance to examine the attached survey results. There are several revealing items that should be the basis for ongoing discussion. We expect to be in touch regularly during the year. Among the plans is a “First Look” video series highlighting top Mass General Brigham Harvard faculty as well as emerging Harvard investigators. As promised, we wanted to also share visual Forum session summaries. You will be able to access the recordings on the Forum’s YouTube page . The first set will go up this morning
We hope you will join us for the 2021 Forum!
Thanks again, Chris
e-Proceedings 2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET
Tweets & Retweets 2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET
Collaborative innovation has never been more important.
2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET
Join top leaders guiding the response, technology and people confronting this century’s greatest health challenge.
Priya Abani
CEO, AliveCor
General Keith Alexander
Co-CEO, IronNet; Former NSA Head
Stéphane Bancel
CEO, Moderna
Marc Casper
CEO, Thermo Fisher
Timothy Ferris, MD
CEO, MGPO; Professor, HMS
John Fernandez
President, MEE; President, Ambulatory Care, Mass General Brigham
John Fish
CEO, Suffolk; BH Board Chair
JF Formela, MD
Partner, Atlas Venture
Jan Garfinkle
Manager Partner, Arboretum Ventures; Chair, NVCA
Phillip Gross
Managing Director, Adage Capital Management
Julia Hu
CEO, Lark Health
Anjali Kataria
CEO, Mytonomy
Roger Kitterman
VP, Managing Partner, Mass General Brigahm Fund
Jonathan Kraft
President, Kraft Group; Chair, MGH Board
Brooke LeVasseur
CEO, AristaMD
Mike Mahoney
CEO, Boston Scientific
Bernd Montag, PhD
CEO, Siemens Healthineers
Kieran Murphy
CEO, GE Healthcare
Elizabeth Nabel, MD
President, BH; Professor, HMS
Matt Sause
CEO, Roche Diagnostics
Peter Slavin, MD
President, MGH; Professor, HMS
Scott Sperling
Co-President, TH Lee; Chair, Mass General Brigham Board
Christopher Viehbacher
Managing Partner, Gurnet Point Capital
Michel Vounatsos
CEO, Biogen
Collaborative Innovation
Together we meet the challenge of the coronavirus and share our commitment to the future of medicine.
FDA Role in Managing Crisis and Anticipating the Next
Elizabeth Nabel, MD
President, Brigham Health; Professor of Medicine, HMS
PANEL
Care in the Next 18 Months
Karen DeSalvo, MD
Chief Health Officer, Google Health
PANEL
Role of AI and Big Data in Fighting COVID-19
Dawn Sugarman, PhD
Assistant Psychologist, Division of Alcohol, Drugs, and Addiction, McLean; Assistant Professor, Psychiatry, HMS
PANEL
Digital Therapeutics
Ann Prestipino
SVP; Incident Commander, MGH; Teaching Associate, HMS
PANEL
Real Time: Front Line Innovation
Hadine Joffe, MD
Vice Chair, Research, Psychiatry; Executive Director, Mary Horrigan Connors Center for Women’s Health and Gender Biology, BH; Paula Johnson Professor, Women’s Health, HMS
PANEL
Digital Therapeutics
Priya Abani
CEO, AliveCor
PANEL
Digital Therapeutics
Julia Hu
CEO, Lark Health
PANEL
Digital Therapeutics
Jan Garfinkle
Manager Partner, Arboretum Ventures; Chair NVCA
PANEL
Early Stage Investment Environment
Anjali Kataria
CEO, Mytonomy
PANEL
Patient Experience During the Pandemic
Brooke LeVasseur
CEO, AristaMD
PANEL
Digital Health Becomes a Pillar
Julie Lankiewicz
Head, Clinical Affairs & Health Economics Outcomes Research, Bose Health
Subject: REGISTRANT RECAP | World Medical Innovation Forum
Dear World Forum Attendee,
On behalf of Mass General Brigham CEO Anne Klibanski MD and Forum co-Chairs Gregg Meyer MD and Ravi Thadhani MD, many thanks for being among the nearly 11,000 registrants representing 93 countries, 46 states and 3200 organizations yesterday. A community was established around many pressing topics that will continue long into the future. We hope you have a chance to examine the attached survey results. There are several revealing items that should be the basis for ongoing discussion. We expect to be in touch regularly during the year. Among the plans is a “First Look” video series highlighting top Mass General Brigham Harvard faculty as well as emerging Harvard investigators. As promised, we wanted to also share visual Forum session summaries. You will be able to access the recordings on the Forum’s YouTube page . The first set will go up this morning
We hope you will join us for the 2021 Forum!
Thanks again, Chris
e-Proceedings 2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET
Tweets & Retweets 2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET
2020 World Medical Innovation Forum – COVID-19, AI – Life Science and Digital Health Investments, MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET
Life science and digital health investments have continued at a strong pace during the COVID-19 crisis. Senior investment leaders discuss what to expect. Will:
Subject: REGISTRANT RECAP | World Medical Innovation Forum
Dear World Forum Attendee,
On behalf of Mass General Brigham CEO Anne Klibanski MD and Forum co-Chairs Gregg Meyer MD and Ravi Thadhani MD, many thanks for being among the nearly 11,000 registrants representing 93 countries, 46 states and 3200 organizations yesterday. A community was established around many pressing topics that will continue long into the future. We hope you have a chance to examine the attached survey results. There are several revealing items that should be the basis for ongoing discussion. We expect to be in touch regularly during the year. Among the plans is a “First Look” video series highlighting top Mass General Brigham Harvard faculty as well as emerging Harvard investigators. As promised, we wanted to also share visual Forum session summaries. You will be able to access the recordings on the Forum’s YouTube page . The first set will go up this morning
We hope you will join us for the 2021 Forum!
Thanks again, Chris
e-Proceedings 2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET
Tweets & Retweets 2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET
e-Proceedings 2020 World Medical Innovation Forum – COVID-19, AI and the Future of Medicine, Featuring Harvard and Industry Leader Insights – MGH & BWH, Virtual Event: Monday, May 11, 8:15 a.m. – 5:15 p.m. ET
Featuring Clinical, Scientific, Tech, AI and Venture Experts
Subject: REGISTRANT RECAP | World Medical Innovation Forum
Dear World Forum Attendee,
On behalf of Mass General Brigham CEO Anne Klibanski MD and Forum co-Chairs Gregg Meyer MD and Ravi Thadhani MD, many thanks for being among the nearly 11,000 registrants representing 93 countries, 46 states and 3200 organizations yesterday. A community was established around many pressing topics that will continue long into the future. We hope you have a chance to examine the attached survey results. There are several revealing items that should be the basis for ongoing discussion. We expect to be in touch regularly during the year. Among the plans is a “First Look” video series highlighting top Mass General Brigham Harvard faculty as well as emerging Harvard investigators. As promised, we wanted to also share visual Forum session summaries. You will be able to access the recordings on the Forum’s YouTube page . The first set will go up this morning
We hope you will join us for the 2021 Forum!
Thanks again, Chris
Mass General Brigham (formerly Partners Healthcare) is pleased to invite media to attend the World Medical Innovation Forum (WMIF) virtual event on Monday, May 11. Our day-long interactive web event features expert discussions of COVID-related infectious disease innovation and the pandemic’s impact on transforming medicine, plus insights on how care may be radically transformed post-COVID. The agenda features nearly 70 executive speakers from the healthcare industry, venture, start-ups, consumer health and the front lines of COVID care, including many of our Harvard Medical School-affiliated researchers and clinicians. The event replaces our annual in-person conference, which we plan to resume in 2021.
Dr. Klibanski will welcome participants to the 2020 World Medical Innovation Forum, a global — and this year, virtual — gathering of more than 5,000 senior health care leaders. This annual event was established to respond to the intensifying transformation of health care and its impact on innovation. The Forum is rooted in the belief that no matter the magnitude of that change, the center of health care needs to be a shared, fundamental commitment to collaborative innovation – industry and academia working together to improve patient lives. No collaborative endeavor is more pressing than responding to the COVID-19 pandemic.
Introduction: Scott Sperling, Co-President, Thomas H. Lee Partners; Chairman of the Board of Directors, Mass General Brigham
Introducing Anne Klibanski – Leadership at its best for breakthroughs in the entire system when return to normalcy
Care in the Next 18 Months – Routine, Elective, Remote
Hospital chief executives reflect on how health care will evolve over the next 18 months in the face of COVID-19. What will routine health care look like? What about elective surgeries and other interventions? And will care-at-a-distance continue to be an essential component? Simply put, how will we provide manage, and pay for health care in a world forever changed by COVID-19?
Moderator: Gregg Meyer, MD, Chief Clinical Officer, Mass General Brigham; Interim President, NWH; Professor of Medicine, HMS
John Fernandez, President, Mass Eye and Ear and Mass General Brigham Ambulatory Care
Out patients decrease in volume now social distancing enabled by using parking lot as waiting rooms
Pre visit and post visit websites will become places of touch – patients accessing via website
COVID-19: Technology Solutions Now and in the Future
Experts leading large teams at the epicenter of the coronavirus outbreak discuss how technology is shaping the pandemic response today and in the coming years. What technology categories are most important? What tools are healthcare organizations, biopharmaceutical companies, and other organizations leveraging to battle this crisis? How will those tools evolve? And, importantly, how can technology inform the medical response to future pandemics? What were the biggest technology surprises in the current response?
mRNA synthetic RNA of Spike protein injected to stir immune response
Phase II working with FDA starting Phase III early Summer
15 mcg dose available in 2020
using own capital to invest to scale up manufacturing no help from Gov’t Grant for clinical trial not for manufacturing
Paul Biddinger, MD, Medical Director for Emergency Preparedness, MGH; Associate Professor of Emergency Medicine, HMS
Sharing information across the system aggregate data technologies
ML as Guidance in resource coordination
David Kaufman, MD, PhD, Head of Translational Development, Bill & Melinda Gates Medical Research Institute
drug development, clinical operations remote monitoring
repurpose compounds usinf libraries
scalability and Global vaccine cheap and available globally
complexity is in coordinations – toolset biology tool RNA mapping viral screening primaru cells and organoids
Outcomes: Aging and co-morbidities
Discovery effort using tools infrastructure maintained between pandemics
Rochelle Walensky, MD, Chief, Infectious Disease, Steve and Deborah Gorlin MGH Research Scholar, MGH; Professor of Medicine, HMS
shared photos important for Public health, using iPhone distribution Demedicalize Testic – not only at clinics but at many placed contact tracing and diagnosis in 24 hours – iPhone is invaluable GPS capability – privacy issues
detect patients with high risk and existing infection monitoring
Public Health – Thermometer given to Patients – data collected centrally any spike and pulse oximeter given to home – remote
Anxiety in opening the economy requires a bit of giving up on privacy
TeleHealth and monitoring remotely
Pharmacy and workplace as points to start Testing vs Order and a nurse call
Digital Health Becomes a Pillar: Tools, Payment, Data
Deployed in the crucible of the coronavirus pandemic, digital health has now become an essential pillar in the delivery of care. Why is that significant? How and why did it happen? What are the essential tools and components? How is the electronic health record and other health data contributing to this digital movement?
Are there novel use cases for telehealth that arose during the first phase of the COVID-19 pandemic? How can digital technologies help enable a full return to work. Thinking ahead to the fall and a possible second wave, are there things we should be doing today to ensure this technology to better detect and profile a resurgence and enhance the patient benefit.
Moderator: David Louis, MD, Pathologist-in-Chief, MGH; Benjamin Castleman Professor of Pathology, HMS
Adam Landman, MD, VP, Chief Information and Digital Innovation Officer, BH; Associate Professor of Emergency Medicine, HMS
COVID-19 call center across Partners, Chat bots automated screening tools, Microsoft assisted 60,000 users of chat bots triaging by screening calls of the Hotline
TeleHealth transformation may be lost due to reimbursement which may not be reimburse after the emergency is over Insurers to incentivize use of of TeleHealth
In person care: Redesign and how to provide In care for the staff and for the Patients
Access problem due to care shortage of specialty care
technology better allocate resources
Industry and Hospital Institutions populations they serve
innovations needs a sustainable economic model for reimbursement
Inequity issues How Telehealth can benefit all of Society, potential for future solutions
Lee Schwamm, MD, Director, Center for TeleHealth and Exec Vice Chair, Neurology, MGH; Vice President, Virtual Care/Digital Health, Mass General Brigham; Professor, Neurology, HMS
Surge capabilities
generate insight
Research and Innovation needs embedding in the enterprise
Bayer Pharma Reflections on Innovation: Creating, Collaborating, and Accelerating Discovery During and After a Pandemic
Dr. Moeller will reflect on how Bayer is weathering the organizational challenges posed by the COVID-19 pandemic. How does a global pharmaceutical company continue to drive drug development when its labs are shut down? What are the critical elements needed to keep the engines of innovation firing even in the face of a global public health crisis? How does a global r&d enterprise plan for an uncertain fall 2020 given a potential return of the virus.
Introduction: John Fish, CEO, Suffolk; Chairman of Board Trustees, Brigham Health
Joerg Moeller, MD, PhD, Head of Research & Development, Pharmaceuticals Division, Bayer AG
led team of 9 products
Unprecedented is COVID-19: effect on work, travel, life
Anti-Malaria vs COVID-19: In China testing early chloroquine approved for RA and anti Malaria Government in China experimental and Bayer supports Clinical Trials by Bill & Melinda Foundation
In 8 weeks most Scientist work from home – amazed what was accomplished by 80% of Bayer working from home
production is kept ongoing anti-infective for Pneumonia
focus on most critical and keep experiment critical and push out studies run Globally – No pre-maturely study was interrupted completely
Great collaboration Flexibility with regulatory agencies in Europe and with FDA – levels not seen before
R&D in Pharma – when out different point than when we started: Opportunities- Compound libraries OPEN after the COVID Pandemic, speed of decision making, team spirit outstanding – levels not seen before
Partnerships: Bayer testing machines and ventilators shared, accelerate mechanisms for new drug development
evidence for repurposing drugs: Chloroquine
Solidarity – everyone are in it TOGETHER, keep that after the Pandemic is over – levels not seen before
The coronavirus outbreak is not only testing health care staff and resources, it is also having an overwhelming impact on patients. This panel will focus on the approach and technologies providers are using to address the patient experience along the continuum of care.
Moderator: Thomas Sequist, MD, Chief Patient Experience and Equity Officer, Mass General Brigham; Professor of Medicine and Health Care Policy, HMS
Video overcome illiteracy and provide personal engagement without the negative
Home health will be the shift – a human component will not go away – sensor technology in car, bathroom
COVID-19 accelerated user adoption of Telehealth
Digital technologies as an equailizer Hispanic patients consumed for information with the new technologies
Daniel Kuritzkes, MD, Chief, Division of Infectious Diseases, BH; Harriet Ryan Albee Professor of Medicine, HMS
conserve PPE impacted Physicians ability to see Patients, Nurses meet patients vs Physicians that delivered care remotely – laying on hands was missing in the care
Masks will not come off but in a while, can’t allow the infection to surge and curtail hospitals from functioning, use mask for the foreseable future
Peter Lee, PhD, Corporate Vice President, Microsoft Research and Incubation
Interactive Chat bots 1 out of 500 hospitals around the Globe adopted the Chat Bot for Patient Intake
Scaling telemetry with feedback loop
iPad at bedside, platform orchestration, new workflows for COVID-19 patients in the backend guiding Patients in the Process was new infrastructure was in the front line
preparing for a game change in Medicine: Patients demanding new experience
Historical context for physicians contribution to care and bridge the digital divide
Jag Singh, MD, PhD, Cardiologist & Founding Director, Resynchronization and Advanced Cardiac Therapeutics Program, MGH; Professor of Medicine, HMS
Isolation is unbearable
Predictive analytics
no going back to before Pandemic
COVID-19 only severe go to hospital
Human contact enhanced interaction with families and Docs
The Role of AI and Big Data in Fighting COVID-19 and the Next Global Crisis – Successes and Aspirations
AI is a key weapon used to fight COVID-19. What are the biggest successes so far? Which applications show the most promise for the future? Can it help a return to work? Can AI help predict and even prevent the next global health care crisis?
Designing for Infection Prevention: Innovation and Investment in Personal Protective Equipment and Facility Design
As with many pathogens, prevention is the best defense against SARS-CoV2, the virus that causes COVID-19. Panelists will discuss the insights, design strategies, technologies, and practices that are emerging to guard against infection and how those innovations are being applied to protect health care providers and their patients.
Based on what was learned during the spring of 2020, are there specific changes that will lessen morbidity and mortality in a potential a second wave?
Moderator: Erica Shenoy, MD, PhD, Associate Chief, Infection Control Unit, MGH; Assistant Professor, HMS
Optimize toward lower cost vs availability of supply
Diverting supply chain to manufacturing not in PPE business
Guillermo Tearney, MD, PhD, Remondi Family Endowed MGH Research Institute Chair, Mike and Sue Hazard MGH Research Scholar, MGH; Professor, Pathology, HMS
3D Printing innovations for filtration capacity of particles, respirators decontaminated, prevention of patient transmission
Negative pressure applied on materials as second line of protection beyond PPE
CPAP to be used
weaning from Ventilators to CPAP
Environment to be protected from air born pathogens
Preparing for Fall 2020 and Beyond: Production, Innovation, Optimization
How does a global medical technology and life sciences company respond to the health challenges posed by COVID-19? Mr. Murphy will reflect on how his organization is working to meet the unprecedented demand for life-saving medical equipment for diagnosing, treating, and managing coronavirus patients. How does a large manufacturer make adjustments to FDA regulated products and supply chains in time to help lessen the impact of a second wave of COVID-19 infections.
Introduction: Jonathan Kraft, President, The Kraft Group; Chair, Mass General Hospital Board of Trustees
90 countries around the Globe – collaborative innovations partnership with GE Health – all assets around the World
Academic with GE Health AI, Diagnostics, data set for ML for Health care
COVID-19 Innovations and Customers needs: Ventilators and
ICU Cloud application with Microsoft to save PPE and Labor, monitor several ICU rooms at once by technology
Quadruple the production and enter new contracts, crisis exposed weaknesses in supply chain of many products
Shortage of PPE was not expected, flexibility and trusted relations with GE Health Suppliers
CT in a BOX – 42 Slices in a container – no exposure to radiation in prefabricated rooms in field hospital requiring no contact with clinicians and rapid response
Command control center with John Hopkins University
Manufacturing facilities in China communicate the situation of the business and the customers needs buyers in the Health care industry
Future for Biotech industry: Modular systems deploy rapidly, test vaccine, SPEED is everything productivity & Speed
Productivity will increase collaboration and speed like partnership with FORD and MIcrosoft
Tech giants are dedicating their vast resources to aid in the global response to the coronavirus. This panel will highlight how the big data and computational power of major tech companies is being deployed to help contain the current pandemic through new technologies and services, enable return to work, and how it could help prevent future ones.
Amanda Goltz, Principal, Business Development, Alexa Health & Wellness, Amazon
Michael Mina, MD, PhD, Associate Medical Director, Molecular Virology, BH; Assistant Professor, Epidemiology, Immunology and Infectious Diseases, Harvard Chan School
Limitations on Viral Testing
Shortage of Swabs for testing
Tech giant: Amazon, Walmart – global reach in supply chain
new collaborations formed on super charge
Antigen test for home administration consumerization of the Testing
Walmart can be positioned for blood tests
Not only Physicians can order tests
Microsoft and Amazon can help in interpretation of the Test using Alexa
Insights on Pandemics and Health Care from the National Security Community
General Alexander, a renowned expert on national security as well as pandemics and health care, will reflect on how AI can help identify and predict future global disease outbreaks and enable fully reopening commerce. He will also discuss what health care systems can learn from the response to COVID-19 to ensure preparedness for the next infectious disease challenge.
Moderator: Gregg Meyer, MD, Chief Clinical Officer, Mass General Brigham; Interim President, NWH; Professor of Medicine, HMS
Calibrating Innovation Opportunity and Urgency: Medical and Social
The social and medical needs of patients are deeply intertwined, yet there are significant gaps in the tools and technologies being developed to help address those needs. These are especially apparent in the non-uniform impact of COVID-19. Harnessing opportunities, particularly for patients whose needs fall into the low medical complexity/high social complexity category — a group often overlooked by health care innovators.
FDA Role in Managing Crisis and Anticipating the Next
The FDA and other regulatory bodies have played a key role in managing the coronavirus pandemic. How will the agency’s priorities shift in the coming months as community transmission (ideally) slows? What is the FDA’s role in return to work? What is the FDA doing to anticipate future health crises? How will these drive new tools and effect that rate of innovation?
Moderator: Ravi Thadhani, MD, CAO, Mass General Brigham; Professor of Medicine and Faculty Dean for Academic Programs, HMS
Biogen CEO Michel Vounatsos will discuss how Biogen is tackling some of society’s most devastating neurological and neurodegenerative disorders, and share his perspective on the impact the global COVID-19 pandemic is having on the biopharmaceutical industry.
Building the Plane While Flying: The Experience of Real-Time Innovation from the Front Line
The COVID-19 crisis has required continuous, real time innovation, impacting the way care is delivered on the front lines and across care continuum. This panel will present the perspective, innovations and experiences of care givers interacting directly with patients across the continuum of care – acute, post-acute, rehab and home care.
Moderator: Ann Prestipino, SVP; Incident Commander, MGH; Teaching Associate, HMS
focussed problem alarms from ventilators were not coordinated till biomed engineers arrives to device a solution
Karen Reilly, DNP, RN, Associate Chief Nursing Officer, Critical Care, Cardiovascular and Surgical Services, BH
Collaborate and move forward
Interdisciplinary team: Physical therapy help quickly
tech to communicate with families
Ready – I wish I had information to stay ahead of the curve
New normal ability to expand and contract
Ross Zafonte, DO, SVP, Research Education and Medical Affairs, SRN; Earle P. and Ida S. Charlton Professor of Physical Medicine and Rehabilitation, HMS
Rehabilitation in Cambridge Spaulding Brighton
Off loading to rehab from other units
Flexibility MGH Brigham – learn to be a new organization
Hotspots optimal mapping
Right person at right challenge
Stay ready for catastrophies
Telecare and Tele rehabilitation – greater benefit on TeleHealth or not who will not benefit from Rehab
CEO Roundtable: Will the Innovation Model Remain as It Was
As we envision a post-COVID-19 world, how will the model for biomedical innovation change? What lessons have been learned? Was this pandemic a once-in-a-lifetime event or should organizations begin to weave pandemic planning into their business and operations strategies? Panelists will discuss these and other related questions.
Emergency and Urgent Care: How COVID-19 Vulnerabilities and Solutions Will Change the Model
How are the roles of emergency medicine and urgent care changing in light of the COVID-19 pandemic? Panelists will discuss this topic as well as how current and anticipated new technologies can aid in the delivery of community, urgent, and emergency care now and in the future.
Given a false negative at the point of care has consequences well beyond the patient being treated, does this change what can be offered in the various patient care settings?
Moderator: Ron Walls, MD, EVP and Chief Operating Officer, BH; Neskey Family Professor of Emergency Medicine, HMS
Accelerating Diagnostics – Maintaining the Priority: Lab, Home and Digital
COVID-19 diagnostics, a linchpin in controlling viral spread — what caused testing in the U.S. to fall so far behind and how can those missteps be prevented in the future? How do the diagnostics industry, and academic medicine, develop the tests that enable group activities including businesses sports, and community? What is the profile of diagnostic tests coming online in the coming months and into next year? What lessons can be learned to guide the global health community in future disease outbreaks? Given the biological complexity, required performance standards, and immense volume is a simple DTC assays possible on a greatly accelerated timeline.
Moderator: Jeffrey Golden, MD, Chair, Department of Pathology, BH; Ramzi S. Cotran Professor of Pathology, HMS
James Brink, MD, Chief, Department of Radiology, MGH; Juan M. Taveras Professor of Radiology, HMS
social determinant of care – communities not able to social distance, multiple languages
Return to Work: Understanding the Technologies and Strategies
Diagnostic testing is a linchpin of the worldwide response to the coronavirus. How does a global leader pivot to develop molecular diagnostics for a novel global pathogen? How does it scale, including managing international supply chains, to provide unprecedented levels of products and services. What are the expectations for return to work and a possible disease spike in fall 2020 or beyond. How will the diagnostics industry be permanently changed.
Moderator: Peter Markell, EVP, Finance and Administration, CFO & Treasurer, Mass General Brigham
Marc Casper, Chairman, President and CEO, Thermo Fisher Scientific
Re-opening the economy requires Testing for certification of health
Testing bringing confidence
PCR – have or have not viral proteins: 5Millions a week, June 10 million tests
antibody testing will also become available in massive scale
Supply chain, more preparedness, robustness of the supply chain
Buying supply in China vs US based
stockpiling by governments not only at the Hospital level vs JIT shocks to the system
Work from home – productivity is good, work from home not ideal environment
Transportation and elevators – social distancing – impossible
Global change enormous Telemedicine ramp up Academic center Telemedicine will prevail
more resilient Health care system dialogue and communications across countries technology will play a role it will improve Health care every where
Digital Therapeutics: Current and Future Opportunities
Digital therapeutics (DTx) represents an emerging class of therapies that is poised for significant growth. Yet already, these software-driven, evidence-based tools for the prevention, management, and/or treatment of disease are already changing patients’ lives. This panel will address how existing DTx are having an early impact — in the COVID-19 pandemic and — and where current development efforts are headed in the coming years especially if there is a aggressive return of the virus in the fall 2020 or later.
Moderator: Hadine Joffe, MD, Vice Chair for Research, Department of Psychiatry, Executive Director, Mary Horrigan Connors Center for Women’s Health and Gender Biology, BH; Paula A. Johnson Professor, Women’s Health, HMS
The investment environment in life sciences and health care overall was at record levels for most of the last decade. What will this environment look like in the wake of the COVID-19 pandemic – especially over the near to mid-term? Will investor priorities and enthusiasm shift? What is the investor role in developing new coronavisurs tests, vaccines, and therapeutics?
Moderator: Roger Kitterman, VP, Venture and Managing Partner, Partners Innovation Fund, Mass General Brigham
Jan Garfinkle, Founder & Manager Partner, Arboretum Ventures
Can you close a deal with out meeting management team
Known funds will prevail vs new funds Parma adjacencies vs medical devices
Telehealth is of interest GI, Cardiovascular
Mental health with TeleHealth
Phillip Gross, Managing Director, Adage Capital Management
Clinical Trial issues
Inflating value of Biotech because therapeutic related to COVID gives a boost
Gregg Meyer, MD, Chief Clinical Officer, Mass General Brigham; Interim President, NWH; Professor of Medicine, HMS
Ravi Thadhani, MD, CAO, Mass General Brigham; Professor of Medicine and Faculty Dean for Academic Programs, HMS
Mass General Brigham (formerly Partners Healthcare) is pleased to invite media to attend the World Medical Innovation Forum (WMIF) virtual event on Monday, May 11. Our day-long interactive web event features expert discussions of COVID-related infectious disease innovation and the pandemic’s impact on transforming medicine, plus insights on how care may be radically transformed post-COVID. The agenda features nearly 70 executive speakers from the healthcare industry, venture, start-ups, consumer health and the front lines of COVID care, including many of our Harvard Medical School-affiliated researchers and clinicians. The event replaces our annual in-person conference, which we plan to resume in 2021.
Artificial Intelligence in Medicine – Part 3: in Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology
Updated on 2/10/2020
Eric Topol
@EricTopol
·
There have only been 5 randomized clinical trials of #AI in medicine to date. Here’s the summary: 4 in gastroenterology (2 @LancetGastroHep, 2 @Gut_BMJ) 1 in ophthalmology (@EClinicalMed) All were conducted in China (None in radiology, pathology, dermatology or other specialties)
The Lancet Gastroenterology & Hepatology publishes high-quality peer-reviewed research and reviews, comment, and news #gastroenterology#hepatology. IF=12.856
Gut Journal
@Gut_BMJ
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Leading international journal in gastroenterology with an established reputation for publishing 1st class research. Find us on Facebook: https://facebook.com/Gut.BMJ
While there are now hundreds of in silico, retrospective dataset reports, the number of prospective (non-randomized) trials in a real clinical environment testing #AI performance is limited. I only know of 11. Let me know if I’m missing any.
Curators: Stephen J. Williams, PhD, Dror Nir, PhD and Aviva Lev-Ari, PhD, RN
Introduction to Part 3: AI in Medicine – Voice of Aviva Lev-Ari & Professor Williams
There is a current consensus that of all specialties in Medicine, Artificial Intelligence technologies will benefit the most the specialty of Radiology.
What AI can do
Of course, there is still a lot AI can do for radiologists. Soonmee Cha, MD, neuroradiologist, has served as a program director at the University of California San Francisco since 2012 and currently oversees 100 radiology trainees, said at RSNA 2019 in Chicago
“we can see a future where AI is improving image quality, decreasing acquisition times, eliminating artifacts, improving patient communication and even decreasing radiation dose.
“If AI can detect when machines are being set up incorrectly and alert us, it’s a win for us and for patients,” she said.
Numerous imaging societies, including the American College of Radiology (ACR) and RSNA, have published a new statement on the ethical use of AI in radiology.
The European Society of Radiology, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics (EuSoMII), Canadian Association of Radiologists and American Association of Physicists in Medicine all also co-authored the statement which is focused on three key areas of AI development: data, algorithms and practice. A condensed summary was shared in the Journal of the American College of Radiology, Radiology, Insights into Imaging and the Canadian Association of Radiologists Journal.
“Radiologists remain ultimately responsible for patient care and will need to acquire new skills to do their best for patients in the new AI ecosystem,” J. Raymond Geis, MD, ACR Data Science Institute senior scientist and one of the document’s leading contributors, said in a prepared statement. “The radiology community needs an ethical framework to help steer technological development, influence how different stakeholders respond to and use AI, and implement these tools to make the best decisions for—and increasingly with—patients.”
“The application of AI tools in radiological practice lies in the hand of the radiologists, which also means that they have to be well-informed not only about the advantages they can offer to improve their services to patients, but also about the potential risks and pitfalls that might occur when implementing them,” Erik R. Ranschaert, MD, PhD, president of EuSoMII. “This paper is therefore an excellent basis to improve their awareness about the potential issues that might arise, and should stimulate them in thinking proactively on how to answer the existing questions.”
Back in September, the Royal Australian and New Zealand College of Radiologists (RANZCR) published its own guidelines on the ethical application of AI in healthcare. The document, “Ethical Principles for Artificial Intelligence in Medicine,” is available on the RANZCR website.
Selective examples of applications of AI in the specialty of Radiology include the following:
RSNA 2019, the world’s largest radiology conference, kicks off at Chicago’s McCormick Place on Sunday, Dec. 1, 2019, and promises to include more AI content than ever before. There will be an expanded AI Showcase this year, giving attendees access to more than 100 vendors in one location.
“Artificial Intelligence and Precision Education: How AI Can Revolutionize Training in Radiology” | Monday, Dec. 2 | 8:30 – 10 a.m. | Room: E450A
“Learning AI from the Experts: Becoming an AI Leader in Global Radiology (Without Needing a Computer Science Degree)” | Tuesday, Dec. 3 | 4:30-6 p.m. | Room: S406B
“Deep Learning in Radiology: How Do We Do It?” | Wednesday, Dec. 4 | 8:30-10 a.m. | Room: S406B
Interview with George Shih, MD, a radiologist at Weill Cornell Medicine and NewYork-Presbyterian and the co-founder of the healthcare startup MD.ai
An academic gold rush, where people are working to apply the latest AI techniques to both existing problems and brand new problems, and it’s all been really great for the field of radiology.
We’re also holding another machine learning competition this year hosted on Kaggle. In previous years, we’ve annotated existing public data that was used for our competition, but this year, we were actually able to acquire high-quality data—more than 25,000 CT examinations that nobody has used or seen before—from four different institutions. The top 10 winning algorithms will also be made public to anyone in the world, which is an amazing way to advance the use of AI in radiology. I think that’s one of the biggest contributions RSNA is making to the academic community this year.
The other exciting part is that our new and improved AI Showcase will include more vendors—more than 100—than any previous year, which shows just how much the market continues to focus on these technologies.
Artificial and augmented intelligence are already helping healthcare improve clinically, operationally and financially—and there is extraordinary room for growth. Success starts with leadership, vision and investment and leaders tell us they have all of the above. Here are the top 7 survey findings.
01 C-level healthcare leaders are leading the charge to AI. AI has earned the attention of the C-suite, with 40% of survey respondents saying their strategy is coming from the top down. Chief information officers are most often managing AI across the healthcare enterprise (27%).
02 AI has moved into the mainstream. The future is now. It’s here. Health systems are hiring data scientists and spending on AI and infrastructure. Some 40% of respondents are using AI, with 50% using between one and 10 apps.
03 Health systems are committed to investing in AI. 93% of respondents agree AI is absolutely essential, very important or important to their strategy. There is great willingness to take advantage of intelligent technology and leverage machine intelligence to enhance human intelligence. Administration holds financial responsibility for AI at 43% of facilities, with IT paying the bill at 26% of sites.
04 Fortifying infrastructure is top of mind. 93% of respondents agree AI is absolutely essential, very important or important to their strategy. There is great willingness to take advantage of intelligent technology and leverage machine
intelligence to enhance human intelligence. Administration holds financial responsibility for AI at 43% of facilities, with IT paying the bill at 26% of sites.
05 Improving care is AI’s greatest benefit. Improving accuracy, efficiency and workflow are the top benefits leaders see coming from AI. AI helps to highlight key findings from the depths of the EMR, identify declines in patient conditions earlier and improve chronic disease management. Cancer, heart disease and stroke are the disease states survey respondents see AI holding the greatest promise—the 2nd, 1st and 5th leading killer of Americans.
06 Health systems are both buying and developing AI apps. Some 50% of respondents tell us they are both buying and developing AI apps. About 38% are exclusively opting to purchase commercially developed apps while 13% are developing everything in-house.
07 Radiology is blazing the AI trail. AI apps for imaging outnumber all other categories of FDA-approved apps to date. It’s no surprise then that respondents tell us that rad apps top the list of tools they’re using to enhance breast, chest and cardiovascular imaging.
Dr. Topol’s new book, Deep Medicine – How Artificial Intelligence Can Make Healthcare Human Again, is an encyclopedia of the emerging Fourth Industrial Age; a crystal ball in what is about happen in the next era of healthcare. I’m impressed by the detailed references and touching personal and family stories.
Centers for Medicare & Medicaid Services (CMS) policy modifications in the past 10 months reveal sweeping changes that fortify Dr. Topol’s vision: May 2018 medical students can document for attending physicians in the health record (MLN MM10412), 2019 ancillary staff members and patients can document the History/medical interview into the health record, 2021 medical providers can document based only on Medical Decision Making or Time (Federal Register Nov, 23, 2018).
Part of making healthcare human is also making it fun. The joy of practicing medicine is about to return to the healthcare delivery as computers will be used to empower humanistic traits, not overburden medical professionals with clerical tasks. For patients, you will be heard, understood and personally treated. Deep Medicine is not a vision of what will happen in 50 years as much will start to reveal within the next 5!
Bravo Dr. Topol!
Michael Warner, DO, CPC, CPCO, CPMA, AAPC Fellow
Academic Gallup Poll: The Artificial Intelligence Age, June 2019.
New Northeastern-Gallup poll: People in the US, UK, and Canada want to keep up in the artificial intelligence age. They say employers, educators, and governments are letting them down. – News @ Northeastern
3.1.5 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place https://worldmedicalinnovation.org/
Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN
3.1.6 Synopsis Days 1,2,3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place
Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN
3.1.8 Unique immune-focused AI model creates largest library of inter-cellular communications at CytoReason. Used to predict 335 novel cell-cytokine interactions, new clues for drug development.
Reporter: Aviva Lev-Ari, PhD, RN
CYTOREASON. CytoReason features in hashtag #DeepKnowledgeVentures‘s detailed Report on AI in hashtag #drugdevelopment report https://lnkd.in/dKV2BB6
3.2.8 Prediction of Cardiovascular Risk by Machine Learning (ML) Algorithm: Best performing algorithm by predictive capacity had area under the ROC curve (AUC) scores: 1st, quadratic discriminant analysis; 2nd, NaiveBayes and 3rd, neural networks, far exceeding the conventional risk-scaling methods in Clinical Use
npj Digital Medicine volume 2, Article number: 77 (2019) | Download Citation
However, “the inconvenient truth” is that at present the algorithms that feature prominently in research literature are in fact not, for the most part, executable at the frontlines of clinical practice. This is for two reasons: first, these AI innovations by themselves do not re-engineer the incentives that support existing ways of working.2 A complex web of ingrained political and economic factors as well as the proximal influence of medical practice norms and commercial interests determine the way healthcare is delivered. Simply adding AI applications to a fragmented system will not create sustainable change. Second, most healthcare organizations lack the data infrastructure required to collect the data needed to optimally train algorithms to (a) “fit” the local population and/or the local practice patterns, a requirement prior to deployment that is rarely highlighted by current AI publications, and (b) interrogate them for bias to guarantee that the algorithms perform consistently across patient cohorts, especially those who may not have been adequately represented in the training cohort.9 For example, an algorithm trained on mostly Caucasian patients is not expected to have the same accuracy when applied to minorities.10 In addition, such rigorous evaluation and re-calibration must continue after implementation to track and capture those patient demographics and practice patterns which inevitably change over time.11 Some of these issues can be addressed through external validation, the importance of which is not unique to AI, and it is timely that existing standards for prediction model reporting are being updated specifically to incorporate standards applicable to this end.12 In the United States, there are islands of aggregated healthcare data in the ICU,13 and in the Veterans Administration.14 These aggregated data sets have predictably catalyzed an acceleration in AI development; but without broader development of data infrastructure outside these islands it will not be possible to generalize these innovations.
3.4.6 HUBweek 2018, October 8-14, 2018, Greater Boston – “We The Future” – coming together, of breaking down barriers, of convening across disciplinary lines to shape our future
3.5 Machine Learning (ML) Algorithms harnessed for Medical Diagnosis: Pattern Recognition & Prediction of Disease Onset
Introduction by Dr. Dror Nir
Icahn School of Medicine at Mount Sinai to Establish World Class Center for Artificial Intelligence – Hamilton and Amabel James Center for Artificial Intelligence and Human Health
First center in New York to seamlessly integrate artificial intelligence, data science and genomic screening to advance clinical practice and patient outcomes.
Integrative Omics and Multi-Scale Disease Modeling— Artificial intelligence and machine learning approaches developed at the Icahn Institute have been extensively used for identification of novel pathways, drug targets, and therapies for complex human diseases such as cancer, Alzheimer’s, schizophrenia, obesity, diabetes, inflammatory bowel disease, and cardiovascular disease. Researchers will combine insights in genomics—including state-of-the-art single-cell genomic data—with ‘omics,’ such as epigenomics, pharmacogenomics, and exposomics, and integrate this information with patient health records and data originating from wearable devices in order to model the molecular, cellular, and circuit networks that facilitate disease progression. “Novel data-driven predictions will be tightly integrated with high-throughput experiments to validate the therapeutic potential of each prediction,” said Adam Margolin, PhD, Professor and Chair of the Department of Genetics and Genomic Sciences and Senior Associate Dean of Precision Medicine at Mount Sinai. “Clinical experts in key disease areas will work side-by-side with data scientists to translate the most promising therapies to benefit patients. We have the potential to transform the way care givers deliver cost-effective, high quality health care to their patients, far beyond providing simple diagnoses. Mount Sinai wants to be on the frontlines of discovery.”
Precision Imaging—Researchers will use artificial intelligence to enhance the diagnostic power of imaging technologies—X-ray, MRI, CT, and PET—and molecular imaging, and accelerate the development of therapies. “We see a huge potential in using algorithms to automate the image interpretation and to acquire images much more quickly at high resolution – so that we can better detect disease and make it less burdensome for the patient,” said Zahi Fayad, PhD, Director of the Translational and Molecular Imaging Institute, and Vice Chair for Research for the Department of Radiology, at Mount Sinai. Dr. Fayad plans to broaden the scope of the Translational and Molecular Imaging Institute by recruiting more engineers and scientists who will create new methods to aid in the diagnosis and early detection of disease, treatment protocol development, drug development, and personalized medicine. Dr. Fayad added, “In addition to AI, we envision advance capabilities in two important areas: computer vision and augmented reality, and next generation medical technology enabling development of new medical devices, sensors and robotics.”
3.5.1.1 Deep Learning extracts Histopathological Patterns and accurately discriminates 28 Cancer and 14 Normal Tissue Types: Pan-cancer Computational Histopathology Analysis
3.5.2.1 Cardiac MRI Imaging Breakthrough: The First AI-assisted Cardiac MRI Scan Solution, HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package
3.5.2.4 CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to anyone on Earth
A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P < .001), the dense area (mean, 38.2 cm2 vs 34.2 cm2; P < .001), and the percentage density (mean, 25.6% vs 24.0%; P < .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P < .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P < .001); this difference was most pronounced for more aggressive cancers.
Conclusion
Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers.
Part 3: Summary – AI in Medicine – Voice of Aviva Lev-Ari & Professor Williams
AI applications in healthcare
The potential of AI to improve the healthcare delivery system is limitless. It offers a unique opportunity to make sense out of clinical data to enable fully integrated healthcare that is more predictive and precise. Getting all aspects of AI-enabled solutions right requires extensive collaboration between clinicians, data scientists, interaction designers, and other experts. Here are four applications of artificial intelligence to transform healthcare delivery:
1. Improve operational efficiency and performance
On a departmental and enterprise level, the ability of AI to sift through large amounts of data can help hospital administrators to optimize performance, drive productivity, and improve the use of existing resources, generating time and cost savings. For example, in a radiology department, AI could make a difference in the management of referrals, patient scheduling, and exam preparations. Improvements here can help to enhance patient experience and will allow a more effective and efficient use of the facilities at examination sites.
2. Aiding clinical decision support
AI-enabled solutions can help to combine large amounts of clinical data to generate a more holistic view of patients. This supports healthcare providers in their decision making, leading to better patient outcomes and improved population health. “The need for insights and for those insights to lead to clinical operations support is tremendous,” says Dr. Smythe. “Whether that is the accuracy of interventions or the effective use of manpower – these are things that physicians struggle with. That is the imperative.”
3. Enabling population health management
Combining clinical decision support systems with patient self-management, population health management can also benefit from AI. Using predictive analytics with patient populations, healthcare providers will be able to take preventative action, reduce health risk, and save unnecessary costs.
As the population ages, so does a desire to age in place when possible, and to maximize not only disease management, but quality of life as we do so. The possibility of aggregating, analyzing and activating health data from millions of consumers will enable hospitals to see how socio-economic, behavioral, genetic and clinical factors correlate and can offer more targeted, preventative healthcare outside the four walls of the hospital.
4. Empowering consumers, improving patient care
As recently as 2015 patients reported physically carrying x-rays, test results, and other critical health data from one healthcare provider’s office to another3. The burden of multiple referrals, explaining symptoms to new physicians and finding out that their medical history has gaps in it were all too real. Patients now are demanding more personalized, sophisticated and convenient healthcare services.
The great motivation behind AI in healthcare is that increasingly, as patients become more engaged with their own healthcare and better understand their own needs, healthcare will have to take steps towards them and meet them where they are, providing them with health services when they need them, not just when they are ill.
Our Summary for AI in Medicine presents to the eReader the results of the 2020 Survey on that topic, all the live links will take the eReader to the report itself. We provided the reference, below
AI in Healthcare 2020 Leadership Survey Report: About the Survey
The AI in Healthcare team embarked on this survey to gain a deeper understanding of the current state of artificial and augmented intelligence in use and being planned across healthcare in the next few years. We polled readers of AI in Healthcare, AIin.Healthcare and sister brand HealthExec.com over 2 months. All data is presented in this report in aggregate, with individual responses remaining anonymous.
The content in this report reflects the input of 1,238 physicians, executives, IT and administrative leaders in healthcare, medical devices and IT and software development from across the globe, with 75 percent based in the United States. The report focuses on the responses of providers and professionals at the helm of healthcare systems, integrated delivery networks, academic medical centers, hospitals, imaging centers and physician groups across the U.S. For a deeper dive into survey demographics, click here.
Some respondents chose to share more specific demographics that help us better get to know our survey base. Those 165 healthcare leaders work for 38 unique health systems, hospitals, physician groups and imaging or surgery centers, across 39 states and the District of Columbia. They are large, small and mid-sized, for profit, not for profit, academic and government owned. Respondents, too, herald from all levels of leadership. Here are some of the interesting titles who chimed in—and we are thankful they did: CEO, CFO, CMO, CIO, chief innovation officer, chief data officer, chief administrative officer, medical director of quality, senior VP of quality and innovation officer, system director of transformation, VP of service line development, and plenty of physicians, directors of ICU, imaging, cath lab and surgery, nurses and technologists.
In this report we unpack current trends in AI and machine learning, drill into data from various perspectives such as the C-suite and the physician leader, and learn how healthcare systems are using and planning to use AI. Turn the page and see where we are and where we’re going.
Author: Mary C. Tierney, MS, Chief Content Officer, AI in Healthcare magazine and AIin.Healthcare
Artificial Intelligence Innovations in Cardiac Imaging
Reporter: Aviva Lev-Ari, PhD, RN
3.3.23 Artificial Intelligence Innovations in Cardiac Imaging, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
‘CTA-for-All’ fast-tracks intervention, improves LVO detection in stroke patients
A “CTA-for-All” stroke imaging policy improved large vessel occlusion (LVO) detection, fast-tracked intervention and improved outcomes in a recent study of patients with acute ischemic stroke (AIS), researchers reported in Stroke.
“Combined noncontrast computed tomography (NCCT) and CT angiography (CTA) have been championed as the new minimum standard for initial imaging of disabling stroke,” Mayer, a neurologist at Henry Ford Hospital in Detroit, and co-authors wrote in their paper. “Patient selection criteria that impose arbitrary limits on time from last known well (LKW) or baseline National Institutes of Health Stroke Scale (NIHSS) score may delay CTA and the diagnosis of LVO.”
“These findings suggest that a uniform CTA-for-All imaging policy for stroke patients presenting within 24 hours is feasible and safe, improves LVO detection, speeds intervention and can improve outcomes,” the authors wrote. “The benefit appears to primarily affect patients presenting within six hours of symptom onset.”
Hsiao said physicians can expect “a little bit of generalization” from neural networks, meaning they’ll work okay on data that they’ve never seen, but they’re not going to produce perfect results the first time around. If a model was trained on 3T MRI data, for example, and someone inputs 1.5T MRI data, it might not be able to analyze that information comprehensively. If some 1.5T data were fed into the model’s training algorithm, though, that could change.
According to Hsiao, all of this knowledge means little without clinical validation. He said he and his colleagues are working to integrate algorithms into the clinical environment such that a radiologist could hit a button and AI could auto-prescribe a set of images. Even better, he said, would be the ability to open up a series and have it auto-prescribe itself.
“That’s where we’re moving next, so you don’t have to hit any buttons at all,” he said.
IBM Watson Health is adding startup DiA Imaging Analysis to its AI Marketplace in an effort to offer clinicians access to more objective and accurate ultrasound analysis, the company announced Dec. 1.
DiA, an IBM Alpha Zone Accelerator Alumni Startup, has developed AI-powered cardiac ultrasound software that’s already been cleared by the FDA. According to a release, the software was designed to help physicians analyze cardiac ultrasound images automatically and more objectively, since image interpretation is inherently a somewhat subjective process.
“Our collaboration with IBM Watson Health demonstrates the implementation of DiA’s vision to make the analysis of ultrasound images smarter and accessible to clinicians with various levels of experience on any platform,” DiA CEO and co-founder Hila Goldman-Aslan said in a statement.
IBM will focus specifically on DiA’s LVivo EF solution, an application with an AI-based quantification solution that provides clinicians with automated clinical data like ejection fraction and global longitudinal strain.
“IBM Watson Health is proud to announce a collaboration with DiA Imaging,” Anne Le Grand, general manager of imaging, life sciences and oncology at IBM, said. “DiA’s innovative AI-powered offerings can provide our clients with the ability to analyze images with advanced AI-based solutions which can support IBM Watson Health’s mission to help build smarter ecosystems.”
U.K.-based health tech firm Ultromics has secured 510(K) FDA clearance for its EchoGo Core image analysis system, the company announced Nov. 14.
EchoGo leverages artificial intelligence to calculate left ventricular ejection fraction, LV volumes and automated cardiac strain on ultrasound-based heart scans. The idea, founder and CEO Ross Upton said, is to automate the analysis and quantification of echos so cardiologists can make more informed decisions about care delivery.
“This is an incredibly exciting step toward the future of healthcare,” Upton, a Forbes “30 Under 30” honoree this year, said in a statement, calling the 510(K) clearance “truly a watershed moment” for his company.
Notably, the FDA’s choice to clear Ultromics’ technology means it will be available to a wider population of patients and providers. Based in the U.K., the company has only been independent of the University of Oxford for two years.
Upton said the EchoGo system will make Ultromics the first tech company to use AI for automated strain analysis, which is applicable to some 60 million scans per year and will be reimbursable in the U.S. starting in January. He said EchoGo could be a useful tool for physicians of all experience levels looking to learn more about strain calculations and improve their interpretation of echocardiograms.
The company is already looking ahead to next year, when Upton and his team plan to launch the EchoGo Pro—something they’re promising will be “the first AI system able to predict cardiac disease from echocardiography.”
“We are also planning to expand into other geographic regions, including Europe and Asia,” Upton said. “Our goal is to improve patient outcomes through earlier detection of cardiac disease.”
According to the study, the finalized model achieved 95% sensitivity and 98% specificity.
Ferrick et al. said that since their training sample size was somewhat small and limited to a single institution, it would be valuable to validate the model externally. Still, their neural network was able to accurately identify CIEDs on chest radiographs and translate that ability into a phone app.
“Rather than the conventional ‘bench-to-bedside’ approach of translational research, we demonstrated the feasibility of ‘big data-to-bedside’ endeavors,” the team said. “This research has the potential to facilitate device identification in urgent scenarios in medical settings with limited resources.”
“Cardiovascular MRI offers unparalleled image quality for assessing heart structure and function; however, current manual analysis remains basic and outdated,” Manisty said in a statement. “Automated machine learning techniques offer the potential to change this and radically improve efficiency, and we look forward to further research that could validate its superiority to human analysis.”
It’s estimated that around 150,000 cardiac MRIs are performed in the U.K. each year, she said, and based on that number, her team thinks using AI to read scans could mean saving 54 clinician-days per year at every health center in the country.
“Our dataset of patients with a range of heart diseases who received scans enabled us to demonstrate that the greatest sources of measurement error arise from human factors,” Manisty said. “This indicates that automated techniques are at least as good as humans, with the potential soon to be ‘superhuman’—transforming clinical and research measurement precision.”
Cardiac MRI Imaging Breakthrough: The First AI-assisted Cardiac MRI Scan Solution, HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package
Reporter: Aviva Lev-Ari, PhD, RN
3.5.2.1 Cardiac MRI Imaging Breakthrough: The First AI-assisted Cardiac MRI Scan Solution, HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 3: AI in Medicine
HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package, the First AI-assisted Cardiac MRI Scan Solution
The future of imaging is here—and FDA cleared.
LOS ALTOS, Calif.–(BUSINESS WIRE)–HeartVista, a pioneer in AI-assisted MRI solutions, today announced that it received 510(k) clearance from the U.S. Food and Drug Administration to deliver its AI-assisted One Click™ MRI acquisition software for cardiac exams. Despite the many advantages of cardiac MRI, or cardiac magnetic resonance (CMR), its use has been largely limited due to a lack of trained technologists, high costs, longer scan time, and complexity of use. With HeartVista’s solution, cardiac MRI is now simple, time-efficient, affordable, and highly consistent.
“HeartVista’s Cardiac Package is a vital tool to enhance the consistency and productivity of cardiac magnetic resonance studies, across all levels of CMR expertise,” said Dr. Raymond Kwong, MPH, Director of Cardiac Magnetic Resonance Imaging at Brigham and Women’s Hospital and Associate Professor of Medicine at Harvard Medical School.
A recent multi-center, outcome-based study (MR-INFORM), published in the New England Journal of Medicine, demonstrated that non-invasive myocardial perfusion cardiovascular MRI was as good as invasive FFR, the previous gold standard method, to guide treatment for patients with stable chest pain, while leading to 20% fewer catheterizations.
“This recent NEJM study further reinforces the clinical literature that cardiac MRI is the gold standard for cardiac diagnosis, even when compared against invasive alternatives,” said Itamar Kandel, CEO of HeartVista. “Our One Click™ solution makes these kinds of cardiac MRI exams practical for widespread adoption. Patients across the country now have access to the only AI-guided cardiac MRI exam, which will deliver continuous imaging via an automated process, minimize errors, and simplify scan operation. Our AI solution generates definitive, accurate and actionable real-time data for cardiologists. We believe it will elevate the standard of care for cardiac imaging, enhance patient experience and access, and improve patient outcomes.”
HeartVista’s FDA-cleared Cardiac Package uses AI-assisted software to prescribe the standard cardiac views with just one click, and in as few as 10 seconds, while the patient breathes freely. A unique artifact detection neural network is incorporated in HeartVista’s protocol to identify when the image quality is below the acceptable threshold, prompting the operator to reacquire the questioned images if desired. Inversion time is optimized with further AI assistance prior to the myocardial delayed-enhancement acquisition. A 4D flow measurement application uses a non-Cartesian, volumetric parallel imaging acquisition to generate high quality images in a fraction of the time. The Cardiac Package also provides preliminary measures of left ventricular function, including ejection fraction, left ventricular volumes, and mass.
HeartVista is presenting its new One Click™ Cardiac Package features at the Radiological Society of North America (RSNA) annual meeting in Chicago, on Dec. 4, 2019, at 2 p.m., in the AI Showcase Theater. HeartVista will also be at Booth #11137 for the duration of the conference, from Dec. 1 through Dec. 5.
About HeartVista
HeartVista believes in leveraging artificial intelligence with the goal of improving access to MRI and improved patient care. The company’s One Click™ software platform enables real-time MRI for a variety of clinical and research applications. Its AI-driven, one-click cardiac localization method received first place honors at the International Society for Magnetic Resonance in Medicine’s Machine Learning Workshop in 2018. The company’s innovative technology originated at the Stanford Magnetic Resonance Systems Research Laboratory. HeartVista is funded by Khosla Ventures, and the National Institute of Health’s Small Business Innovation Research program.
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