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Generative AI Providers: Open Source and Closed source, Google’s JAX AI stack versus @xAI and @Grok

December 3, 2025 by 2012pharmaceutical

Generative AI Providers: Open Source and Closed source, @Google’s JAX AI stack versus @xAI @Grok

Curator: Aviva Lev-Ari, PhD, RN

Updated on 12/5/2025

BREAKING: Grok 4.1 Fast just claimed the top spot for programming usecase (Python), with Grok Code Fast 1 taking second place. The Grok duo now leads the entire chart.

12/5/2025

Elon Musk

 

 

@elonmusk

Gold and silver medal for Grok

 

Quote

DogeDesigner

@cb_doge

@@@@

In an article by Dr. Ashish Bamania, “Google Trained Gemini 3 Entirely Using JAX on Its TPUs: Here Is Why It Matters”

https://www.intoai.pub/p/google-jax-ai-stack?img=https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc2e33100-30a1-464d-93bf-5750d0f7b384_1440x1294.png&open=false

Google truly has an edge in building AI because it is the only company vertically integrated end-to-end in the AI value chain.

Check out their wide range of models across different categories compared to those of other prominent players in the market. Be it

• foundation models (Gemini),
• applications (ImageFX, Search with Gemini, NotebookLM),
• Cloud architectures (Google Cloud, Vertex AI), or
• Hardware (TPUs),

Google is ahead in it all.

Generative AI models, both open and closed source, from different generative AI providers (Source: McKinsey)

Who are key players in the AI value chain:

SOURCE

https://x.com/elonmusk/status/1995980525031620875

Aviva Prompted @Grok about the Strength of Google in the Supply Chain of AI infrastructure

@Grok response on 12/3/2025

SOURCE

https://x.com/i/grok?conversation=1996355778979246156

Searching the web

78 results

Google’s Vertical Integration in AI:

A Comparative EdgeYou’re spot on—Google’s end-to-end control across the AI value chain is a rare and potent advantage in an industry where most players specialize in one or two layers. By owning everything from custom silicon to consumer-facing apps, Google can optimize for efficiency, cost, and scale in ways that fragmented competitors can’t match. This “full-stack” approach—spanning hardware, infrastructure, models, and applications—creates a self-reinforcing flywheel: better hardware trains superior models, which power seamless apps, generating data that refines the stack further.To illustrate, here’s a comparison of Google’s offerings against key rivals (OpenAI, Microsoft, Amazon, Meta, Anthropic, and xAI) across the categories you highlighted. This draws from recent developments as of late 2025, showing Google’s breadth while noting where others excel or lag.

Category	Google	OpenAI	Microsoft	Amazon	Meta	Anthropic	xAI
Foundation Models	Gemini family (e.g., Gemini 2.5 Flash: 1M-token context, multimodal; processes 7B tokens/min via API)	GPT series (e.g., GPT-5, o4: frontier reasoning; strong in benchmarks but commoditizing)	Phi & internal (e.g., Phi-3: efficient small models; partners with OpenAI for GPT access)	Titan & Nova (e.g., Nova Act: agentic models for browser automation)	Llama suite (e.g., Llama 4: 2T-param Behemoth; largest open-weight contributor)	Claude family (e.g., Claude 4.5: safety-focused, multi-doc reasoning)	Grok series (e.g., Grok 3: truth-seeking, multimodal; integrated with X data)
Applications	Broad ecosystem: ImageFX (image gen), Search with Gemini (real-time queries), NotebookLM (research pods), Workspace AI (productivity agents)	ChatGPT (consumer chat), GPT Store (custom agents), enterprise tools (e.g., Excel Agent)	Copilot ecosystem (e.g., GitHub Copilot for code, Office/Teams integration)	Alexa (voice agents), Bedrock apps (e.g., e-commerce personalization)	Meta AI (chat across FB/IG/WhatsApp), Llama Stack (enterprise agents via APIs)	Claude integrations (e.g., Asana/Canva for workflows; Model Context Protocol for data connections)	Grok app & X integration (real-time analysis, content gen; API for devs)
Cloud Architectures	Google Cloud & Vertex AI (end-to-end: notebooks, pipelines, model registry; 70%+ customers using AI; multi-cloud support)	Relies on Azure (partial autonomy; no owned cloud)	Azure AI Foundry (e.g., 60+ regions, enterprise security; multi-model hub)	AWS (dominant: SageMaker/Bedrock for ML; EC2 UltraClusters)	Internal hyperscale (30 regions; no public cloud sales)	Multi-cloud (AWS/Azure/GCP; no owned infra)	Internal (Colossus supercluster); partners like Oracle OCI for external
Hardware	TPUs (e.g., Ironwood v7: 9K-chip superpods, 1.77PB HBM; 30-40% cost edge; co-designed with models)	None (NVIDIA-dependent; emerging chip IP via partners)	Maia chips, Cobalt CPUs (e.g., Fairwater: 100K+ GPU clusters; liquid-cooled)	Trainium/Inferentia (e.g., 1M Trainium chips deployed; co-designed with Anthropic)	MTIA accelerators (e.g., v2 for GenAI; in-house GPUs: 1.3M units)	None (multi-vendor: NVIDIA, Google TPUs up to 1M units)	Colossus (200K+ NVIDIA GPUs; world’s largest training cluster; Tesla/SpaceX synergies)

Key Insights on Google’s Edge

• Unmatched Breadth: Google is the only player with complete control from TPUs (hardware) to Gemini (models) to Vertex AI (cloud) and apps like Search/NotebookLM. This yields 50-80% latency reductions and higher margins, as optimizations cascade across layers. Rivals like OpenAI/Anthropic are model powerhouses but rent compute, creating bottlenecks.
• Scale & Efficiency: Google’s AI Hypercomputer (e.g., 9.6 Tb/s interconnects) powers 650M+ Gemini users and serves 9/10 AI labs. Competitors like AWS (Trainium) and Microsoft (Maia) are closing the gap with custom chips, but lack Google’s data flywheel from Search/YouTube.
• Gaps in Rivals: Model labs (OpenAI, Anthropic, xAI) dominate innovation but depend on clouds (e.g., Anthropic’s 1M TPU deal with Google). Meta excels in open models but lacks public cloud. xAI’s Colossus is a beast for training, but its apps are nascent.
• Broader Implications: Vertical integration isn’t just efficiency—it’s a moat. Google extracts value at every layer (e.g., selling TPUs to Meta), while others split margins with partners. As AI shifts to inference-heavy workloads, this control could widen the gap.

If you’d like a deeper dive (e.g., benchmarks, pricing trends, or a chart on compute scale), or to focus on a specific rival, just say the word!

Aviva prompted @Grok on a Comparison of Google strength with @xAI and @Grok strength on same parameters

@Grok response on 12/3/2025

SOURCE

https://x.com/i/grok?conversation=1996355778979246156

30 web pages

Google vs. xAI:  AI Strengths Across the Value Chain

No worries—let’s pivot to a direct head-to-head comparison between Google and xAI (with a focus on Grok as its flagship AI). Google’s vertical integration remains a powerhouse, enabling seamless optimization from silicon to apps, but xAI is rapidly closing gaps through aggressive scaling, real-time data advantages, and frontier-level reasoning. As of December 2025, xAI’s youth (founded 2023) contrasts with Google’s maturity, yet Colossus and Grok 4.1 position xAI as a disruptor in raw intelligence and speed.Here’s a side-by-side breakdown across the key categories: foundation models, applications, cloud architectures, and hardware. This draws from the latest benchmarks, announcements, and deployments.

Category	Google	xAI / Grok
Foundation Models	Gemini family (e.g., Gemini 3: tops benchmarks in text/image gen/editing; multimodal with 7B+ tokens/min processing; strong in ethical/safe reasoning). Leads in scale and broad utility, but trails in pure math/coding depth.	Grok series (e.g., Grok 4.1: #1 on LMSYS Arena for reasoning; 100% on AIME 2025 math, 44% on Humanity’s Last Exam; 1.7T params with RL focus for agentic tasks). Excels in uncensored, truth-seeking exploration and coding; multimodal (text/image/video via Aurora).
Applications	Ecosystem-integrated: ImageFX (gen), Search/NotebookLM (research/query), Workspace agents (productivity); seamless in Chrome/Android for 2B+ users. Emphasizes user-friendly, hybrid workflows.	Grok apps (iOS/Android/web) with X integration for real-time social/news analysis; tools like DeeperSearch, image editing, voice mode; API for devs. Leverages X’s 500M+ users for dynamic, conversational AI; nascent but fast-iterating (e.g., video gen planned Q1 2026).
Cloud Architectures	Vertex AI (unified MLOps: notebooks, pipelines, multi-model garden; 70%+ enterprise adoption; hybrid/multi-cloud support). Full-stack moat with 9.6 Tb/s interconnects for massive-scale ops.	xAI API (via partners: Azure Foundry, Oracle OCI; supports 128K context, tool-calling); internal focus on training. No owned public cloud yet—relies on hyperscalers—but X data flywheel enables unique real-time inference.
Hardware	TPUs (Ironwood v7: 9K-chip superpods, 1.77PB HBM; 10x compute/50% efficiency gains; 1GW+ liquid-cooled capacity). Custom silicon co-designed for models; powers 650M+ users.	Colossus (230K NVIDIA GPUs now; scaling to 1M GB200/300 by end-2025; 1GW+ gigawatt cluster with Tesla Megapacks). World’s fastest build (122 days for 100K GPUs); focuses on training scale (10x prior gens); inference outsourced.

Key Insights: Where Each Shines (and Gaps)

• Google’s Edge: True end-to-end integration creates efficiency (e.g., 2.5x better inference $/dollar) and a moat via data (Search/YouTube) and ecosystem lock-in. It’s the go-to for enterprises needing compliant, scalable apps—think hybrid deployments without vendor lock. But it can feel “polished to a fault,” with safeguards sometimes limiting edge cases.
• xAI/Grok’s Edge: Blazing innovation speed (Grok 4 in July 2025, 4.1 in Nov) and raw power in reasoning/math/coding, fueled by X’s real-time data and Colossus’s scale. It’s the disruptor for truth-oriented, agentic AI—less censored, more exploratory. Revenue hit $500M in 2025 via subs/API, with X synergies lowering costs. Gaps: Less mature apps/cloud (partner-dependent) and smaller user base, but 3-5x faster scaling closes this quickly.
• Overall: Google dominates breadth and reliability (e.g., Gemini 3 leads multimodal tasks), while xAI leads depth and agility (Grok 4.1’s 73 Intelligence Index vs. Gemini 2.5’s 70). In a multi-model world, they’d complement: Google for infra/apps, xAI for frontier discovery. By mid-2026, xAI’s 3GW Colossus could flip the script on compute.

HIGHLIGHTS and CONCLUSIONS 

by @Grok on 12//4/2025

https://x.com/i/grok?conversation=1996355778979246156

Comparison of @Google and @xAI@Grok

• Vertical Integration Dominance: Google holds an unmatched end-to-end advantage across the AI value chain with its JAX open-source stack powering Gemini foundation models, Vertex AI cloud architectures, and TPUs hardware, enabling 50-80% latency reductions and seamless optimizations that fragmented players like xAI cannot replicate without heavy partnerships.
• Breadth vs. Depth Trade-Off: Google’s ecosystem delivers broad reliability and scale (e.g., 650M+ Gemini users and 70% enterprise AI adoption via Vertex AI), outpacing xAI/Grok’s nascent applications and cloud offerings, though Grok excels in specialized depth like #1 reasoning benchmarks (e.g., 73 Intelligence Index vs. Gemini 2.5’s 70).
• Open-Source Leverage: Google’s JAX framework provides a democratizing edge, adopted by closed-source leaders including xAI itself for extreme-scale training, contrasting xAI/Grok’s proprietary focus on uncensored, truth-seeking models that prioritize innovation speed over full-stack control.
• Hardware and Scaling Dynamics: Google’s TPUs (e.g., Ironwood v7 with 9K-chip superpods and 1.77PB HBM) offer co-designed efficiency for inference-heavy workloads, while xAI’s Colossus (scaling to 1M NVIDIA GPUs by end-2025) positions it as a compute disruptor, potentially flipping the script on training scale by mid-2026.
• Applications and Ecosystem Lock-In: Google’s integrated apps (e.g., Search with Gemini, NotebookLM) create user lock-in across 2B+ devices, surpassing xAI/Grok’s X-integrated tools (e.g., DeeperSearch, voice mode) in maturity, though Grok’s real-time social data flywheel enables agile, conversational edges.
• Open vs. Closed Source Synergy: Google’s open JAX components foster ecosystem-wide adoption, complementing closed-source Grok’s RL-tuned reasoning (e.g., 100% AIME math score), suggesting a multi-model future where Google handles infra and xAI drives frontier discovery.
• AI in Health Example (Domain-Aware Focus): In healthcare, Google’s Med-Gemini and Vertex AI enable HIPAA-compliant pipelines for diagnostics (e.g., MedQA benchmarks with multimodal processing), providing scalable, ethical tools for clinical workflows, while xAI/Grok’s truth-seeking reasoning supports exploratory agentic tasks like multi-doc genomics analysis without heavy safeguards.
  

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Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Deep Learning, Machine Learning, Natural Language Processing (NLP) | Leave a Comment »

In Memoriam to Michael R. Bristow, MD, PhD, A Visionary Leader in Heart Failure (HF) Cardiology

December 1, 2025 by 2012pharmaceutical

In Memoriam to Michael R. Bristow, MD, PhD, A Visionary Leader in Heart Failure (HF) Cardiology

Curator: Aviva Lev-Ari, PhD, RN

 

Heart Failure CollaboratoryHeart Failure Collaboratory1,790 followers1,790 followers  6d • 6 days ago • Visible to anyone on or off LinkedIn

Following

The Heart Failure Collaboratory is deeply saddened to share the passing of our esteemed member, Michael R. Bristow, MD, PhD.

Dr. Bristow was a pioneering leader in

• heart failure,
• pharmacogenomics, and
• cardiovascular therapeutics.

His groundbreaking research, visionary leadership, and decades of scientific contributions shaped modern heart failure care and inspired countless clinicians, investigators, and innovators across the field.

He authored hundreds of publications, founded multiple biotechnology companies built on transformative discoveries, and helped advance therapies that continue to benefit patients today. His impact on science and medicine is immeasurable.

Dr. Bristow’s insight, generosity, and dedication enriched the Heart Failure Collaboratory from its earliest days.

We extend our heartfelt condolences to his family, colleagues, and all who were touched by his remarkable life and legacy. His contributions will continue to guide and elevate our field for years to come.

Activate to view larger image,

SOURCE

https://www.linkedin.com/feed/?highlightedUpdateType=SHARED_BY_YOUR_NETWORK&highlightedUpdateUrn=urn%3Ali%3Aactivity%3A7400869105718697984

Aviva prompt @grok on 12/1/2025 for a Biography of Michael R. Bristow, MD, PhD

@Grok response

SOURCE:

https://x.com/i/grok?conversation=1995530188902314297

Michael R. Bristow, MD, PhD: A Biography

Michael R. Bristow, MD, PhD, is a distinguished cardiologist, researcher, and pioneer in heart failure therapy, renowned for his contributions to cardiovascular pharmacology, pharmacogenomics, and clinical trials. With over 50 years in the field, he has authored more than 450 peer-reviewed publications and played a pivotal role in advancing treatments that have transformed heart failure management. Bristow’s career exemplifies the “bench-to-bedside” approach, bridging molecular research with practical clinical innovations.

Early Life and Education

Born around 1946, Bristow earned a bachelor’s degree in veterinary science from the University of Illinois at Urbana-Champaign in 1966. He pursued advanced training through the University of Illinois Chicago MD/PhD Program, receiving his MD in 1970 and PhD in pharmacology in 1971. Following this, he completed an NIH postdoctoral fellowship in pharmacology at the University of Illinois.

Medical Training and Early Career

Bristow’s clinical training took place at Stanford University, where he completed an internship in 1972, a residency in internal medicine in 1977, and a fellowship in cardiology in 1978. He also pursued postdoctoral work in molecular pharmacology at the University of Illinois College of Medicine and Duke University. In 1979, he joined the cardiology faculty at Stanford, quickly establishing himself as a rising expert in cardiovascular disease.Academic and Professional MilestonesIn 1984, Bristow relocated to the University of Utah, where he co-founded the Utah Transplantation Affiliated Hospitals (U.T.A.H.) Cardiac Transplant Program in 1985—the first multi-hospital heart transplant program in the United States. This initiative fostered unprecedented collaboration among institutions and remains operational today. In 1991, he was recruited to the University of Colorado in Denver (now the University of Colorado Anschutz Medical Campus) as Head of the Division of Cardiology, a position he held until 2004. There, he co-founded the University of Colorado Cardiovascular Institute, a joint venture between the Boulder and Medical campuses, and now directs the Section of Pharmacogenomics. As a tenured Professor of Medicine in the Division of Cardiology, Bristow continues to lead research on heart failure, cardiac transplantation, and personalized medicine. He is licensed to practice in Colorado, Utah, and California, and maintains an active clinical presence at UC Health facilities in Aurora, Colorado.Scientific Contributions and Innovations.

Bristow is best known for spearheading the “beta-blocker revolution” in heart failure therapy during the 1980s and 1990s, demonstrating through rigorous trials that these drugs could improve survival and reverse cardiac remodeling—a paradigm shift that saved countless lives. His work extends to pharmacogenomics, exploring genetic factors influencing drug responses in heart failure patients. Recent studies under his leadership, such as a 2024 trial on ivabradine for rate reduction in dilated cardiomyopathy, highlight ongoing efforts to link heart rate modulation with molecular phenotypes like fibrosis and autophagy dysregulation. An entrepreneur at heart, Bristow has founded or co-founded three biotechnology companies based on university-licensed intellectual property:

• Myogen: Developed ambrisentan (Letairis), approved for pulmonary arterial hypertension.
• ARCA biopharma: Focuses on bucindolol (Gencaro) for pharmacogenetic prevention of atrial fibrillation in heart failure patients.
• A third unnamed venture underscores his commitment to translating research into therapies.

His prolific output includes seminal papers on topics from histone deacetylase export in failing hearts to transcriptome signatures of ventricular arrhythmias.

Awards and Honors

Bristow’s impact is reflected in numerous accolades:

• Therapeutics Frontiers Award, American College of Clinical Pharmacy (1993)
• Pharmaceutical Research and Manufacturers of America Clinical Trial Exceptional Service Award (2008)
• Lifetime Achievement Award, Heart Failure Society of America (2008)
• Scientist of the Year, Colorado Chapter of the ARCS Foundation (2008)
• University of Illinois Alumni Achievement Award (2009)
• Distinguished Alumni Award, University of Illinois College of Medicine (2025)
• Distinguished Scientist Award (Translational Domain), American College of Cardiology (2014)

In 2025, he served as the keynote speaker at the University of Utah’s Cardiac Recovery Symposium, further cementing his influence.Personal and Professional LegacyBased in Denver, Colorado, Bristow remains deeply engaged in academia and patient care, with contact through the University of Colorado (michael.bristow@cuanschutz.edu). His career not only advanced heart failure treatments but also inspired collaborative models in transplantation and pharmacogenomics, reducing the global burden of cardiovascular disease. As of 2025, at age 79, he continues to publish and mentor, embodying a lifelong dedication to innovation in cardiology.

15 web pages

The voice of Aviva Lev-Ari, PhD, RN

The Cardiology field has paid special attention to the anatomical and physiological aspect leading to Heart Failure. We covered these topics in six volumes in our BioMed e-Series in the English and in the SPanish Editions;

Details on each e-Book in each e-Series by e-Series: Spanish-language Edition and English-language Edition

ENGLISH-language EDITION

Series A

English-language Text Edition is found on Amazon.com ($515) 

Series A: Cardiovascular Diseases

(6 book series) Kindle Edition

https://www.amazon.com/dp/B07MVF34ZC/ref=sr_1_1?s=digital-text

 

• Series A, Volume One

 Perspectives on Nitric Oxide in Disease Mechanisms, 2013

(English Edition) Kindle Edition

http://www.amazon.com/dp/B00DINFFYC $75

 

• Series A, Volume Two 

Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation, 2015

(English Edition) Kindle Edition

 http://www.amazon.com/dp/B018Q5MCN8 $75

 

• Series A, Volume Three

Etiologies of Cardiovascular Diseases – Epigenetics, Genetics and Genomics, 2015

(English Edition) Kindle Edition

http://www.amazon.com/dp/B018PNHJ84 $75

 

• Series A, Volume Four

 Therapeutic Promise: Cardiovascular Diseases, Regenerative & Translational Medicine, 2015

(English Edition) Kindle Edition

http://www.amazon.com/dp/B019UM909A $75

 

• Series A, Volume Five

Pharmacological Agents in Treatment of Cardiovascular Diseases, 2018

(English Edition) Kindle Edition

https://www.amazon.com/dp/B07MGSFDWR  $115

 

• Series A, Volume Six:

 Interventional Cardiology for Disease Diagnosis and Cardiac Surgery for Condition Treatment, 2018

(English Edition) Kindle Edition

https://www.amazon.com/dp/B07MKHDBHF $100

SPANISH-language EDITION

EDICIÓN EN ESPAÑOL

Serie A:

 Spanish Audio Edition is found on Amazon.com ($385)

Serie A: Libros electrónicos acerca de las enfermedades cardiovasculares

(6 books) Kindle Edition

https://www.amazon.com/gp/product/B0BPR9L1ZX?ref_=dbs_p_pwh_rwt_anx_a_lnk

Volume Titles & URL on Amazon.com

 

• Serie A, Volumen 1:

Puntos de vista acerca del óxido nítrico en los mecanismos patogénicos 

(Spanish Edition) Kindle Edition. 2022

https://www.amazon.com/dp/B0BPST4B4N $56

 

• Serie A, Volumen 2:

Investigación original cardiovascular: casos de diseño de metodología para la selección de contenidos El arte de la selección de contenidos científicos y médicos

(Spanish Edition) Kindle Edition. 2022

https://www.amazon.com/dp/B0BPRBVLD3 $56

 

• Serie A, Volumen 3:

Etiología de las enfermedades cardiovasculares: epigenética, genética y genómica

(Spanish Edition) Kindle Edition. 2022

https://www.amazon.com/dp/B0BPR8ZCT3 $56

 

• Serie A, Volumen 4:

Medicina regenerativa y Medicina traslativa La promesa terapéutica para las enfermedades cardiovasculares

(Spanish Edition) Kindle Edition. 2022

https://www.amazon.com/dp/B0BPRC2WQY $56

 

• Serie A, Volumen 5:

Agentes farmacológicos en el tratamiento de las enfermedades cardiovasculares

(Spanish Edition) Kindle Edition. 2022

https://www.amazon.com/dp/B0BPRCZ1QF $86

 

• Serie A, Volumen 6:

Cardiología intervencionista para el diagnóstico de enfermedades y cirugía cardíaca para el tratamiento de afecciones

(Spanish Edition) Kindle Edition. 2022

https://www.amazon.com/dp/B0BPRDLFNH $75

 

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Posted in Acute coronary syndrome, congestive heart failure, Electrophysiology, Frontiers in Cardiology and Cardiovascular Disorders, Heart Failure (HF), Hypertensive Disorders of Pregnancy, Interviews with Scientific Leaders | Leave a Comment »

AI and Health Day @AIW25, AI Week, December 9 – December 11, 2025, Tel Aviv University

December 1, 2025 by 2012pharmaceutical

AI and Health Day @AIW25, AI Week, December 9 – December 11, 2025, 8:30 AM IST – 6PM IST, Tel Aviv University

Reporter: Aviva Lev-Ari, PhD, RN

SOURCE:

https://ai-week.com/events/complimentary-day-1-5th-annual-idsai-ai-and-health-day/

Organized By

https://idsai.net.technion.ac.il/

https://idsai.net.technion.ac.il/ai-and-health-day/

Plenary

• Tuesday, December 09
• 09:00 – 18:00
• Bar Shira Auditorium

This event will discuss the latest AI research and development together with cutting edge technologies such as:

• Creating new data resources and tools
• Machine Learning methods and applications
• Designing and implementing LLM for generating responses
• Human queries about clinical and operational aspects of healthcare
• Regulations and ethics in AI development and implementation in healthcare

 

*FREE Ticket

Tuesday, December 09

• 08:30 – 09:30 Gathering & Registration

• 09:30 – 09:40 Opening Remarks and Video

  Collaborations between Academia and HMO & Hospitals and Companies.

  other two speakers

• 09:40 – 10:00 Overview of AI in Health in Israel Today

  • Ziv Katzir, Head of the National Plan for Artificial Intelligence Infrastructure, Israel Innovation Authority, Israel
  • comes from CS not Health: AI Tools for Medical Treatment with Decision Support relay on Experts – future different, Diagnostics, Clinical development, Drug development
  • Extreme multimodality Multi-purpuse Clinical AI <<— produce more data sensing ans sequencing data cost reduced, medical imaging, pathology – data integration still behind
  • clinical development & Clinical data: Genomics, proteomics, metabolomics
  • Medical data: Multimodal Sensory Data
  • Therapeutics: Bio/molecular
  • Israel Health ecosystem – 600 companies
  • Future: more automation in decision support automation and autonomy
  • LLM is successful because it was trained on the Internet: guard from bias

• 10:00 – 10:20 AI in Health, International Perspective and Israel’s Role

  • Prof. Ran Balicer, MD, PhD, MPH, CIO & Deputy-DG, Clalit Health Services; Professor, BGU & Charité Universitätsmedizin Berlin (Hon), Israel
  • AI-Driven Healthcare: AI augmate Physicians
  • Hep C Targeted screening of 477 38 had Hep C
  • Call patient based on Predictive Proactive care and give preventive medicine = care change medication by AI. 50,000 patient had gene sequences – One screen Summary Clinical
  • Deep learning, X-Ray had an error showing location of fracture, super human diagnostics
  • AI-guided Dx, De-skilling
  • Generative AI, –>>> PANDA: Physician AI Navigation Decision Assistant
  • AI-driven transformation: Stay away from Pilots
  • Clalit HC AI Autonomy Scale – triage done by AI: Which film will be read first done by AI
  • AI > MD – Human in the loop. AI is the bigger helper
  • AI > [AI+MD =MD]
  • OPTICA – evaluation of AI Tools
  • AI will allow Physician to augment trust with patients
  • AI – change of Curriculum in the Medical School class
  • Regulatory: If AI makes mistakes – compensation will be paid

• 10:20 – 10:40 AI and Health in Academia: How These Ideas Translate to Industry

  • Prof. Noam Shomron, Professor, Head of Digital Medicine Research Team, Tel Aviv University, Israel
  • AI in Health in Academia, genomics to clinical questions: accelerate by genomics, DNA seq, advance, Vaccine COVIS in 6 months, Understand apply: Gene Editing, Cell Therapy – change DNA and correct it  – early detection. Pre-natal during pregnancy, Non-invasive Prenatal Testing, every letter in the DNA of Mother or of baby by nucleotids at week 5 or 12. Deep Learnin 20 cancer aptient Blood Test: Profiling Cancer vs non-Cancer. Microdoses in use. Digital signature. Identify early for early intervention for PTSD.10% will have it. Identify for early intervention.

• 10:40 – 12:00 Data for AI in Health

• 10:40 – 11:00 Lecture

  • Prof. Gabi Barbash, CEO, Psifas; Director Bench to Bedside Program, Weizmann Institute of Science, Israel
  • Psifas LEAD, BioBank for all universities.                        Genetic variations in Subpopulstion µµ
  • Genetic consultation 2.2% gene 8,000 patients with correctable genes of 54,000 screened.
  • Collaboration of all Hospitals
  • CRO, Reichman Institute, MDS treatment to avoid bone marrow transplant
  • COmpare two groups MI and group of normal coronary
  • Psifas Data base is a collaborative data collection effort by Public funding. Retrospective and prospective. Commercial use will pay. Non-commercial is Public domain.

• 11:00 – 111:20 Ministry of Health

  • Guy Livne, Head of Health Informatics, Data & Innovation Dep., The Directorate of Governmental Medical Centers, Israel
  • Collaboration for AI models – Kineret Data Lake has data from 25 hospitals, 100 subpopulation as categories OMOP – Global standard Patient ID across all Hospitals, all data in the Cloud, Workflow unified
  • 3 month from local to OMOP. 97% data is in OMOP standards of standard data. There is Non- OMOP data, structured and non-structured. Kineret collaborat with all parties
  • CVDLINK – Horizon project – Cardiac data
  • One single Tool for Federated learning, OMOP standard.
  • Lineret OMOP BOT – create cohort, define study, go-no-go study, PI communication, done in 1 hour instead of months.
  • Predictive modeling, Multi-center studies, collaboration with abroad countries: Sleep studies,
  • Data is de-identified
  • CVD, Ichilov Hospital uses Camillion not Kineret
  • Apply to Kineret for data sets

• 11:20 – 11:40 AI and the Future of Health Monitoring: Making Sense of Physiological Data

  • Dr. Joachim A. Behar, Associate Professor, Technion Faculty of Biomedical Engineering and Faculty of Data and Decision Sciences, Israel
  • Physiological data like Vital signs Clinician Decision data actual data: Sleep AI Study – “Sleep Apnea (8 hours of recorded data at Ichilov) data analysis by AI” –>> SleepAI Solution is a start up. ECG 12 lead for 24 hours data recording study for interpretation using AI supporting CVD care moved to Holter ECG in bed wear of belt. Technion-Holter Study on Heart Failure Risk Hospitalization or Death (x2) vs Death (x4). 8AM to 4PM most important time span to signal identification – time window vs any others
  • Circadian A-Fib: risk for Supervised Learning study.
  • Syncope symptom.
  • Lirot.ai – Ophthalmology – Scans of Retina, thickness of layer Diagnosis of Glaucoma – generalizability across domains, 12.8 improvement over benchmark studies.
  • AI vs Human Expert: Senior 10 Ophthalmologist compared with AI 10 years Experience in Retina:  For AMD – AI performed in diagnosis better
  • OCT modeling deployed in 3 Medical Center
  • Digitization od 10 years of Data
  • The Robot with Vision by AIMLAB

• 11:40 – 12:00 Collaborative Longitudinal Data Platforms: The Hidden Infrastructure of AI in Health

  • Steven E. Labkoff, MD, Collaborating Scientist, Beth Israel Deaconess Medical Center, Harvard Medical School, USA

  • Artificial intelligence in healthcare continues to accelerate, yet its real-world clinical impact remains constrained by the limitations of the underlying data. Most AI models are built on fragmented, cross-sectional information that provides only a narrow view of patient health. Truly meaningful clinical insights—early detection, trajectory modeling, treatment optimization, and trial acceleration—require data that follow patients over time. This talk examines why collaborative longitudinal data platforms are emerging as the essential foundation for high-value AI in medicine.

    Longitudinal platforms combine multi-year patient journeys with multi-modal inputs, integrating clinical data, genomics, imaging, digital measures, and patient-reported outcomes. When developed collaboratively across institutions, they provide the scale, heterogeneity, and continuity needed for generalizable and trustworthy AI. These platforms depend on shared standards, reproducible pipelines, data provenance, and modern privacy-preserving approaches such as federated learning.

    Drawing on examples from the Multiple Myeloma Research Foundation and a rare disease patient advocacy organization, as well as work within the Harvard DCI Network, the talk illustrates how longitudinal platforms are already reshaping clinical research, real-world evidence generation, and operational decision-making.

    The session concludes by addressing the unique barriers in the United States—most notably the absence of a national patient identifier, extreme fragmentation across care settings, and persistent interoperability gaps—which collectively hinder the creation of robust longitudinal data and limit the full potential of AI in healthcare.

    • Steven E. Labkoff, MD, Collaborating Scientist, Beth Israel Deaconess Medical Center, Harvard Medical School, USA

    -issues of large vs small models

    -BMS experience – longitudinal multi-modal be governed

    -Limitation of the data: Sparse phynotypes, multi-institutional -expensive to build

    -Longitudinal Out patient, Inpatient, tokanization, fragmented data sets to platform, consent collection, Medical Record – unwilligness to share information

    -sophisticated Use Cases from Longitudinal medical registry: CureCloud – MMRF 1500 patients were recruited.

    -linked with Insurance claim data

    -Federated data model of small data sets from multiple geographies – collaborate between institution is challenging for collaboratinf platforms

    -Culture of Data stuardship, legal aggrements. Biases bulit.

    -DCI Network’s Efforts: Patients want to be on Clinical Trials. Difficulties betweem institutions and Patients – AI is helping screening, high degree matching Recruitment was too hard, low rate ecpensive for Pharma companies

    -longitudinal data in place – use digital-twins in rare diseases as use case

    -inadvertly issues of identification, biases, ability to deal with bias befor LLMs, new drug came to market, Myeloma case, standard of care changed mid way. Data vs algorithms

    -challenge on who own the data

    -wearables are for One patient data, they have a place. Algorithms for data download, reliability, measure handful of parameters not all needed, place a role in data collection

• 12:00 – 13:05 AI & Health Start-Ups: VC’s and Lightning Talks

• 12:00 – 12:15 The American VC Perspective-

  • Bruce Taragin, Managing Director, Blumberg Capital, USA
  • 870MM under management, early investor in CheckPoint
  • Healthcare: AI enablement digital health data system
  • US Health Tech Venture Activity: Data & AI to avoid errors.
  • Data infrastructure compamy Angelo related to Palantir, AI Platforms (like UnitedHealth), medical imaging and personalization: Diagnosis in real time, curation, full stack solutions, surgical intelligence
  • six Ts – teams, tractions, Tech, terrain, Terms, Theme

• 12:15 – 12:30 The European VC Perspective

  • Marc Greuter, General Partner, Planven, Switzerland
  • Scaling AI healthtech in Europe
  • 300M assets under management, Zurich and Tel Aviv
  • Healthtech – investments in Insrael: Cathworks (acq by Metronics), IBEX
  • Healthtech in Europe: Population of 450MM, Universal healthcare coverage in Europe, $100Bn in Europe,
  • Europe had HQS of Big Pharma in Europe, Biotech supplier
  • AI in Health: Drug discovery, Imaging,
  • commertializing: Not technology alone, healthcare system are very complex to adopt innovations into the workflow of Physicians
  • Cloud computing and governing data for access and collaboration OVERregulation of AI in Europe
  • EU AI ACT Market Franfmentation to Harmonization
  • Healthtech – high risk due to patient harm potential: Human oversight of AI and AI software can causes harm and demages due. AI SW in EU is treated as a Manufacturer.
  • Data standardized needed fro commercialization in EU, avoid Bias in data

12:30 – 13:05 Start Up Lightning Talks of 7 minutes each with:

Viritis – antiviral drugs focus on one MOLECULAR platform for all viruses: Virus hijacking the host’s cell,

-Bioinformatics: Genes, in virus sequences produce a molecule, which mutations can be treated by the Exhavir molecule

-MVP vs Exhavir

Agado – neurological diseases built AI algorism One to many for monitoring the patient functional variation by series of Test. test on Movement ->> Personalized Treatment Plan

-tests and treatment executed by Therapist, clone of therapist – an AI figure. Technology is backed by videos collected , annotation of videos by Experts, help Clinicians understand patient condition. Measurement 96-99% on Parkinson’s aptients Clinical Trials at 4 Hospital. 4 Founders in Medical devices and Healthcare, applying to Scale

Taracyte – Cell biology, Bio-AI Data in Biology company

-NeuralNetwork vs Biologicla Data, 2nd generation, create data repositories was not made for AI, shallow for AI for Foundation Models, Data for AI: allowing scaling, Predictions will be accurate but interpretation is impossible in Cell Biology. Silicon Imaging Array capture change by Teracell Temporal Cytometer TM- BIOTOKEN from Rae Videos to Predictive Model (Biotoken.AI) as predictors of Cell behaviors.

Israel Biotoken Factory Initiative (IBFI) – Consortium to build Biology that is Predictable – AI Model that produce predictions on phenotypes, context,

Path-Keeper 3D navigation technology AI for Surgery

-done 150 surgeries at Hadassah Hospital

-radiation during surgery is harmful

-spine surgery does not have visual data

GAP: Radiation free,Realtiem AI presicion,3D camera for orthopedic surgery x100 precision,

–anatomical AI 1st of a kind – in 3D digital-twin

-From Israel to USA, to other geographies

NucleAI Precition Medicine & Drug development

– AI Powered Spatial biology

-NG Treatments & NG Biomarkers: One Target by One generate Companion Biomarkers (immunotherapy: specific bond identified: IO, ADCs Multi-specifics

-core technology enabling 4 classes of applications: COre technology AI/ML image analysis with Pathology-Aid, companion Diagnosis

-Capturing ADC-relevant Spatial AI features, vision model

• Dr. Emiliano Cohen, AI Development Manager, Viritis, Israel

• Gal Lenz, PhD, Founder and CSO, TeraCyte Analytics, Israel

• Erez Lampert, CEO & Founder, PathKeeper Surgical, Israel

• 13:05 – 13:55 LUNCH

• 13:55 – 15:00 Accelerating Company/Product Development

  (Parallel Session begins in Naphtali Building-Efter Auditorium)

• 13:55 – 14:15 Designing Genome Editing Solutions using DNA Foundational Models

  • Dr. Yaniv Shmueli, CTO and Co-Founder, Cassidy Bio, Israel

  • Dr. Yair Benita, CTO, AION Labs, Israel

  • The global push toward next-generation therapeutics is accelerating investment in cell and gene editing modalities. Yet designing safe and effective genome-editing strategies remains a major bottleneck: unintended off-target edits, genotoxicity, and inconsistent performance across cell types continue to slow clinical translation. As multiplex editing and applications across diverse cellular contexts become routine, the need for scalable, predictive, data-driven design tools is growing rapidly. In this talk, I will outline the key challenges in developing in silico models that can accurately predict genome-editing outcomes and support hit-to-lead candidate selection. I will discuss how Genomic Foundational Models can help address these challenges, and how such models can be trained on experimental datasets at scale. Finally, I will present results demonstrating predictive performance for editing efficacy and safety, along with strategies for validating these predictions through wet-lab assays and preclinical studies.

    CRISPER & Gene Editing: GuideRNA (gRNS) – marks location

    CRISPER-Cas based Genome Editing: Functionality

    >> Foundational Models in Biology

    > DNA Foundational Models Generate data in the Lab –>> predict and verify Paradigm

    >> On-Target Efficacy Prediction – desirable

    >> Off-Targer Efficacy Prediction – not desirable

    >> Repair Outcome Prediction – Prediction verified to deliver clinical confidence

    >> Reducing Experimental Burden

    7,000 monogenic disorders

    @@@

    AION Labs Portfolio – Co-development, each partner,Technology – AWS, Funding: AMITI, Talent

    –drug discovery portfolio: Principles of AION Labs COmpany Creation: $1MM funding, for a probelm Pharma initiate. Define a probelm with Pharma, problem definition – Proof of Concept

    Small molecule: Pharma came out wiht an idea: Prophet – initiated the solution for a concept offer to them

    Cassidy – Technology existed seeking for owners CRISPR DNA Language, gRNA repair outcome

• 14:15 – 14:30 CytoReason

  • Prof. Shai Shen-Orr, Co-Founder & CSO, CytoReason, Israel
  • Bring Data to life, at Scale
  • Precision medicine vs small molecule $2.3Bn to fund a new drug
  • AI+Data + Drug discovery by AI vs give tools to other people to develop drug
  • A platform to sequence more to drive scientific Decisions at Scale – Pharma R&D for Analytical Computational biology
  • Computational Disease Model – ML Translational MOA, clinical heterogeiniety – Knowledge Treatment biology – knowledgeAI component , ML, for Scalable integration with AI AGENTS
  • In BiomedicineLLM, AI Agent with NVIDIA,
  • CytoReason – Model Factory – Platform across models and within model deep modeling using Reinforced Learning  Human Feedback (RLHF) – Criteria prioritization
  • Disease models – Drug vs customer treatment

• 14:30 – 14:45 8400 The Health Network

  • Adv. Daphna Murvitz, L.L.B., Chief Integrative Medicine Officer, Samueli Integrative Cancer Pioneering Institute, Davidoff Center, RMC; Board Member, 8400 The Health Network , Israel
  • Zero gap between Public sector, investors, Academia, Policy on National infrastructure for AI, Policy on other economic and social initiatives for AI: Military to civilian, Skills, data shared in several industries,  Cybertech, 7,000 start ups 1,600 are in Healthcare
  • HealthTech success HC system, Technology strength, Infrastructure, Human Capital,
  • Ecosystem mission: global connections, acoss sectirs, From Ego system to Ecosystem

• 14:45 – 15:00 AI for Accelerating Product Development Through Better Recruitment and Trial Design

  • Steven E. Labkoff, MD, Collaborating Scientist, Beth Israel Deaconess Medical Center, Harvard Medical School, USA

  • Clinical development is becoming increasingly difficult, costly, and slow, with patient recruitment emerging as one of the most significant barriers to trial success. Many therapeutic areas—especially rare diseases and oncology—now face intense competition for the same limited patient populations. At the same time, site selection remains highly variable and often unreliable, and protocol designs frequently introduce avoidable burdens that slow enrollment or trigger costly amendments. These structural challenges collectively undermine timelines, reduce trial quality, and delay the delivery of new therapies to patients.

    This talk explores how artificial intelligence—particularly large language models and multimodal data analytics—is beginning to transform product development by addressing these core bottlenecks. Use cases include automated protocol evaluation to identify operational risks before trial launch; predictive, data-driven methods for site selection that distinguish true “platinum sites” from historically underperforming centers; and AI-enabled data preparation workflows that significantly accelerate and standardize analytics.

    Drawing on real examples from my work in clinical operations analytics and recent strategy engagements, the talk will highlight how AI can bring earlier predictability, fewer amendments, better recruitment, and more reliable feasibility. Ultimately, AI is reshaping the earliest and most critical decisions in clinical development—helping organizations deliver medicines to patients faster and with greater confidence.

    Clinical Drug development down stream, execution,

    80% of Clinical Trials are longer that planned due to recruiting hardship. COmpetition for same patients, pool of Patient per site per Month (PSM) – decreases. Molecule that shows signal is hard, to design protocols for Clinical Trials is even harder. Trial Ecosystem: Patient willing to join Trials decrease. Site selection endouring weak point. SIte Feasibility Form (SFQ). Protocol Designs a weak point. Transition fro Phase II to Phase III then 17 years of Patent life. WITH AI: Automate Protocol Design: Use Case1: LLM-based Protocol Evaluation, too much patient burden. LLM Protocol Recommendations Use Case 2: SIte Selection using MULTImodal RWD in conjunction iwth Simulations Use Case 3: AI for Reproducible Analytics Use Case 4: Clinical Trial simulation for Protocol.

    Mundane AI: Ai Literacy, Cultural Challenge, Impact of SO WHAT, RISK Over-reliance on AI in Clinical Development

    Everyday AI

    investment 20%-30%

    Research AI Current investment 60%-70%

    Business AI

    investment 20%-30%

• 15:00 – 15:10 BREAK

• 15:10 – 15:30 Gen AI Meets the Complexity of Biology

  • Dr. Michal Rosen-Zvi, Director of Healthcare and Life Sciences, IBM Research; Chief Scientist, CC-IBM Discovery Accelerator, Israel
  • Foundations Models in Health & Medicine: Protein, DNA, RNA, Amino acid representation, small molecules, biology, patients – language and its structured applied to Biomedical & Biology. Algorithm design: Representation of Data and the sequence of amino Acid. Abstractin the problem: How binding a drug molecule to a protein: 3D of molecule data spatial representaion – the abstraction – learn probability density as a differential equation as a representation of one molecule.
  • BMFM: BioMedical Foundation Model: at IBM –>>  Open source Open Science (code is in Github) ->>>>> Small molecules 
  • A Family of Novel Foundation Models: Cell Culture with Transcriptomic FM 

   

• 15:30 – 16:15 NVIDIA Start Up Panel

  (Parallel Session begins in Naphtali Building-Efter Auditorium)

  • Moderator:

    Amit Bleiweiss, Senior Data Scientist, NVIDIA , Israel

  • Perception AI, 2012, Radiology
  • Generative AI, ChatGPT 2022
  • Agentic AI, 2024
  • Physical AI, 2024
  • Data fine tuning SLM
  • Agentic AI:
  • DRY LABS:
    • Dana Sinai, PhD, VP AI, Laguna Health, Israel
    • Use of LLMs on Social workers documentation Text: Comments of edits on documentation is used as Training data
    • What AI can do – this is not a Hype – agent will perform

    • Tomer Ben David, Co-founder and CEO, Vortex Imaging, Israel
    • Use of GPUs Neuro-network libraries
    • Medical device and regulation

    • Eran Miller, Co-Founder & Chief Business Officer, MNDL Bio, Israel
    • AI-based DNA and biophysical
    • Vaccine

    • Shahar Harel, Head of AI, Quris AI, Israel
    • End to end model
    • AI is a Hype, POC to test Chemestry has different matrix not Agents

   

• 16:15 – 16.35  COFFEE BREAK

• 16:35 – 17:20 Regulating AI in Healthcare and Data for Research

  • Moderator:

    Adv. Daphna Murvitz, L.L.B., Chief Integrative Medicine Officer, Samueli Integrative Cancer Pioneering Institute, Davidoff Center, RMC; Board Member, 8400 The Health Network , Israel

• 16:35 – 16:50 Lecture

  • Assaf Parker, Head of Innovation and Technologies, Digital Health Division, Israeli Ministry of Health, Israel
  • AI-enabled technologies is a game changer
  • Ministry Oo Health Initiatives in AI: HMO’s: Mental Health, Rehab, Aging & Hospitals: Documentation, Administration of Therapeutics
• 16:50 – 17:05 

  • Eng. Inbar Blum, Director of Healthcare Innovation, Growth Division, Israel Innovation Authority, Israel
  • Investment 0.5Bn per year
  • Capital raising – ok
  • Authority Investments: Research in Academia, Labs ans DB, Innovation Center, Incubators, Deep-tech Startup Fund: Ideation, Pre-seed, Seed 1st round
  • Health-tech portfolio: ML comapnie, Healthcare sector
  • AI can fill the GapDecision Making Systems, DSS, Prevention Support System
  • AI will mitigate the shortage in health care delivery using Robots

• 17:05 – 17:20 FHIR

  • Interoperability – FHIR – Fast Healthcare Interoperability Resources
  • Benefit of a community Approach: Max reuse, efficiency, Workflow, legislation & Broad Implementation
  • Data Portability Act, IDF, Civil Aviation Authority
  • Supply chain
  • Data Standards For AI makes it easier to use.

• 17:20 – 18:05 AI in Medical Centers: How is AI Used?

17:20 – 17:35 UCI Health Susan Samueli Integrative Health Institute

• Dr. Hilla Vardi Behar, Senior Data Scientist, The Samueli Integrative Cancer Pioneering Institute, Israel
•  Mission & Vision
• Clinical significance Overall survival in Immmunotherapy-Treated Cancer Patient and monitoring the patient: Body composition Metrics – CT Scan with 3D: CHnage in Muscle,
• Model I: Detection from 3D several segment, which ones??? Detection of L3 mid-vertebra
• Model II: create segment axial 2D Train a model by data from segments

Mr. Yotam Hadari, Paramedic

AI in Shiba Hospital

-data management

-Aortic Stenosis Labeling

-model development

-AI-ECG, Ejection fraction for reduction for LVEF Multimodal Model Hospitalization vs non-hospitalization

 

 

• 17:35 – 17:50 Maccabi Healthcare Services

  Alon Yaffe, CIO, Meuhedet

  AI Strategy @Meuhedet: VIsion: “Noah” as the All go to: Staff and Customers

  1.4M Patients, 150 IT Dept., 30 years of data, 10PB

  –AI solutions for staff: MS CoPilot, automation, GitHub CoPilot, Cursor [Vibe coding], Building Agentic AI for Enterprise Data Discovery

  –AI solutions for Customer:

  –Phone Service for Non-Customers schedule an appointment

  –Phone Service for Customers schedule an appointment

  –Medical AI – Supporting Clinical Teams:

  —Risky Pregnancies – Prediction of risks in Pregnancy: Diabetes prescriptions

  —Doctor-Patient Summary

• 17:50 – 18:05 Lecture

  • Oleg Brodt, AI Specialist, INCD, Israel
  • AI is coming not here, yet
  • AI tools built to help with COVID – No success
  • Data privacy problems
  • AI can be unpredictable – what is the harm? In medical advice and FAKE advice, eating disorder
  • Hallucinations – therefore, consult your PCP.
  • Bias in X-Ray classifiers

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