Limitations of large language models in medical applications
Bibliographic record
Abstract
Dear Editor, We read with great interest the article by Cuthbert and Simpson [1], which evaluated Chat Generative Pre-trained Transformer’s (ChatGPT’s) ability to pass the Fellowship of the Royal College of Surgeons examination in Trauma and Orthopaedic Surgery. In light of the recent surge of positive literature advocating for the incorporation of large language models (LLMs) in medical practice and education [2, 3], it is equally important to highlight their shortcomings as well. In this letter, we aim to supplement the authors’ study with a brief discussion of LLMs’ technical constraints that hinder their implementation and use in clinical and educational environments. To understand LLMs’ limitations, it is essential to first discuss their mechanism of action. These models undergo training on vast quantities of textual data; in the case of ChatGPT (and its foundational GPT model), these sources include Wikipedia entries, web pages, and online book corpora. Through extensive training, LLMs develop the ability to produce human-like responses to natural inputs ranging from simple queries to complex instructions.
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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 | 0.057 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".