ChatGPT applied to Cardiovascular diseases: Diagnosis and Management

9/20/2024

DASI Simulations, OH-based company gained FDA clearance for an artificial intelligence (AI) Product that identifies and measures cardiac structures in CT scans

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2024/09/20/dasi-simulations-oh-based-company-gained-fda-clearance-for-an-artificial-intelligence-ai-product-that-identifies-and-measures-cardiac-structures-in-ct-scans/

Israeli vendor AISAP gained FDA clearance for its new AI-enabled, point-of-care ultrasound (POCUS) software platform, AISAP Cardio

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2024/09/20/israeli-vendor-aisap-gained-fda-clearance-for-its-new-ai-enabled-point-of-care-ultrasound-pocus-software-platform-aisap-cardio/

JACC editor ‘very important moment’ for Cardiology: New drugs for obesity and prevention, New tools for structural heart analysis for Heart Failure, AI harnessed for Cardiac patient monitoring

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2024/09/20/jacc-editor-very-important-moment-for-cardiology-new-drugs-for-obesity-and-prevention-new-tools-for-structural-heart-analysis-methods-for-heart-failure-ai-harnessed-for-cardiac-monitoring/

4/29/2024

TAVR or SAVR? ChatGPT could help cardiologists decide

Michael Walter | April 22, 2024 | Cardiovascular Business | TAVR

One positive takeaway from the large language model’s performance was the fact that it did not recommend any medical treatment patients for surgery, “which confirms its ability to identify patients at high or prohibitive surgical risk.” Not everything about its assessments, however, was positive.

“The algorithm still suggested TAVR for seven patients assigned to medical treatment, illustrating the difficulty and uncertainty surrounding such a decision,” the authors wrote. “Usually, the decision to choose medical management is mainly related to excessive frailty, the number of comorbidities, or expected limited life expectancy, suggesting the futility of an invasive procedure. However, this final decision is complex and potentially not fully represented by the 14 variables used in the present study.”

Reviewing their findings, Salihu et al. emphasized that these advanced AI models could potentially serve as a “failsafe” when managing care for patients with severe AS. The HT would still ultimately make any decisions, but ChatGPT or a similar large language model may provide value by running in the background in case the HT makes some sort of error with its judgement.

SOURCES

Original study:

Adil Salihu, David Meier, Nathalie Noirclerc, et al. A study of ChatGPT in facilitating Heart Team decisions on severe aortic stenosis. EuroIntervention. 2024 Apr 15;20(8):e496-e503.

https://cardiovascularbusiness.com/topics/clinical/structural-heart-disease/tavr/tavr-or-savr-chatgpt-could-help-cardiologists-decide?utm_source=related_content&utm_medium=related_content&utm_campaign=related_content

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3/7/2024

Machine Learning to Infer a Health State Using Biomedical Signals — Detection of Hypoglycemia in People with Diabetes while Driving Real Cars

Published January 31, 2024

NEJM AI 2024;1(3)

DOI: 10.1056/AIoa2300013

VOL. 1 NO. 3

https://ai.nejm.org/doi/full/10.1056/AIoa2300013?query=ai_wu&cid=DM2329175_Non_Subscriber&bid=2144480931

Abstract

BACKGROUND

Hypoglycemia, one of the most dangerous acute complications of diabetes, poses a substantial risk for vehicle accidents. To date, both reliable detection and warning of hypoglycemia while driving remain unmet needs, as current sensing approaches are restricted by diagnostic delay, invasiveness, low availability, and high costs. This research aimed to develop and evaluate a machine learning (ML) approach for the detection of hypoglycemia during driving through data collected on driving characteristics and gaze/head motion.

METHODS

We collected driving and gaze/head motion data (47,998 observations) during controlled euglycemia and hypoglycemia from 30 individuals with type 1 diabetes (24 male participants; mean ±SD age, 40.1±10.3 years; mean glycated hemoglobin value, 6.9±0.7% [51.9±8.0 mmol/mol]) while participants drove a real car. ML models were built and evaluated to detect hypoglycemia solely on the basis of data regarding driving characteristics and gaze/head motion.

RESULTS

The ML approach detected hypoglycemia with high accuracy (area under the receiver-operating characteristic curve [AUROC], 0.80±0.11). When restricted to either driving characteristics or gaze/head motion data only, the detection performance remained high (AUROC, 0.73±0.07 and 0.70±0.16, respectively).

CONCLUSIONS

Hypoglycemia could be detected noninvasively during real car driving with an ML approach that used only data on driving characteristics and gaze/head motion, thus improving driving safety and self-management for people with diabetes. Interpretable ML also provided novel insights into behavioral changes in people driving while hypoglycemic. (Funded by the Swiss National Science Foundation and others; ClinicalTrials.gov numbers, NCT04569630 and NCT05308095.)

4/13/2023

Performance of ChatGPT as an AI-assisted decision support tool in medicine: a proof-of-concept study for interpreting symptoms and management of common cardiac conditions (AMSTELHEART-2)

View ORCID ProfileRalf E. Harskamp,

doi: https://doi.org/10.1101/2023.03.25.23285475

This article is a preprint and has not been peer-reviewed [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.

ABSTRACT

Background It is thought that ChatGPT, an advanced language model developed by OpenAI, may in the future serve as an AI-assisted decision support tool in medicine.

Objective To evaluate the accuracy of ChatGPT’s recommendations on medical questions related to common cardiac symptoms or conditions.

Methods We tested ChatGPT’s ability to address medical questions in two ways. First, we assessed its accuracy in correctly answering cardiovascular trivia questions (n=50), based on quizzes for medical professionals. Second, we entered 20 clinical case vignettes on the ChatGPT platform and evaluated its accuracy compared to expert opinion and clinical course.

Results We found that ChatGPT correctly answered 74% of the trivia questions, with slight variation in accuracy in the domains coronary artery disease (80%), pulmonary and venous thrombotic embolism (80%), atrial fibrillation (70%), heart failure (80%) and cardiovascular risk management (60%). In the case vignettes, ChatGPT’s response matched in 90% of the cases with the actual advice given. In more complex cases, where physicians (general practitioners) asked other physicians (cardiologists) for assistance or decision support, ChatGPT was correct in 50% of cases, and often provided incomplete or inappropriate recommendations when compared with expert consultation.

Conclusions Our study suggests that ChatGPT has potential as an AI-assisted decision support tool in medicine, particularly for straightforward, low-complex medical questions, but further research is needed to fully evaluate its potential.

SOURCE

https://www.medrxiv.org/content/10.1101/2023.03.25.23285475v1

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