A Primer on Large Language Models (LLMs) and ChatGPT for Cardiovascular Healthcare Professionals
Bibliographic record
Abstract
Generative artificial intelligence (AI), particularly large language models (LLMs), such as ChatGPT, is transforming healthcare by offering novel ways to synthesize and communicate medical knowledge. This development is especially relevant in cardiology, as patient education, clinical decision-making, and administrative workflows play pivotal roles in this area. ChatGPT, originally built on GPT-3 and refined into GPT-4, can simplify complex cardiology literature, translate technical explanations into plain language, and address questions across different linguistic backgrounds. Studies show that although ChatGPT demonstrates considerable promise in performing text-based tasks-ranging from passing portions of the European Exam in Core Cardiology to creating patient-friendly educational materials-its inability to interpret images remains a major limitation. Meanwhile, concerns around false information, data bias, and ethical issues highlight the need for careful oversight. Future directions include integrating LLMs with computer-vision modules for image-based diagnostics and combining unstructured patient data to improve risk prediction and phenotyping. Social-media research suggests that chatbots sometimes provide more-empathetic responses than do physicians, underscoring both their potential advantages and complexities. LLM-based tools can also generate letters for insurance prior authorizations or appeals, helping reduce administrative burden. New multimodal approaches, such as ChatGPT Vision, have the potential to enable direct image processing, although clinical validation of this function is yet to be established. The judicious integration of ChatGPT and other LLMs into cardiology requires ongoing validation, robust regulatory frameworks, and strong ethical guidelines to ensure patient privacy, avoid misinformation, and promote equitable healthcare delivery. This review aims to provide a primer on LLMs for cardiovascular professionals, summarizing key applications, current limitations, and prospects in this rapidly evolving field of digital health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".