Principles of artificial intelligence and its application in cardiovascular medicine

Using Large Language Models to Generate Educational Materials on Childhood Glaucoma

PURPOSE

To evaluate the quality, readability, and accuracy of large language model (LLM)-generated patient education materials (PEMs) on childhood glaucoma, and their ability to improve existing the readability of online information.

DESIGN

Cross-sectional comparative study.

METHODS

We evaluated responses of ChatGPT-3.5, ChatGPT-4, and Bard to 3 separate prompts requesting that they write PEMs on "childhood glaucoma." Prompt A required PEMs be "easily understandable by the average American." Prompt B required that PEMs be written "at a 6th-grade level using Simple Measure of Gobbledygook (SMOG) readability formula." We then compared responses' quality (DISCERN questionnaire, Patient Education Materials Assessment Tool [PEMAT]), readability (SMOG, Flesch-Kincaid Grade Level [FKGL]), and accuracy (Likert Misinformation scale). To assess the improvement of readability for existing online information, Prompt C requested that LLM rewrite 20 resources from a Google search of keyword "childhood glaucoma" to the American Medical Association-recommended "6th-grade level." Rewrites were compared on key metrics such as readability, complex words (≥3 syllables), and sentence count.

RESULTS

All 3 LLMs generated PEMs that were of high quality, understandability, and accuracy (DISCERN ≥4, ≥70% PEMAT understandability, Misinformation score = 1). Prompt B responses were more readable than Prompt A responses for all 3 LLM (P ≤ .001). ChatGPT-4 generated the most readable PEMs compared to ChatGPT-3.5 and Bard (P ≤ .001). Although Prompt C responses showed consistent reduction of mean SMOG and FKGL scores, only ChatGPT-4 achieved the specified 6th-grade reading level (4.8 ± 0.8 and 3.7 ± 1.9, respectively).

CONCLUSIONS

LLMs can serve as strong supplemental tools in generating high-quality, accurate, and novel PEMs, and improving the readability of existing PEMs on childhood glaucoma.

Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management

The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

Artificial Intelligence in Heart Failure: Friend or Foe?

In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.