Revolutionizing Cardiology With Words: Unveiling the Impact of Large Language Models in Medical Science Writing
Bibliographic record
Abstract
Large language models (LLMs) are a unique form of machine learning that facilitates inputs of unstructured text/numerical information for meaningful interpretation and prediction. Recently, LLMs have become commercialized, allowing the average person to access these incredibly powerful tools. Early adopters focused on LLM use in performing logical tasks, including-but not limited to-generating titles, identifying key words, summarizing text, initial editing of scientific work, improving statistical protocols, and performing statistical analysis. More recently, LLMs have been expanded to clinical practice and academia to perform higher cognitive and creative tasks. LLMs provide personalized assistance in learning, facilitate the management of electronic medical records, and offer valuable insights into clinical decision making in cardiology. They enhance patient education by explaining intricate medical conditions in lay terms, have a vast library of knowledge to help clinicians expedite administrative tasks, provide useful feedback regarding content of scientific writing, and assist in the peer-review process. Despite their impressive capabilities, LLMs are not without limitations. They are susceptible to generating incorrect or plagiarized content, face challenges in handling tasks without detailed prompts, and lack originality. These limitations underscore the importance of human oversight in using LLMs in medical science and clinical practice. As LLMs continue to evolve, addressing these challenges will be crucial in maximizing their potential benefits while mitigating risks. This review explores the functions, opportunities, and constraints of LLMs, with a focus on their impact on cardiology, illustrating both the transformative power and the boundaries of current technology in medicine.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".