Limitations of large language models in medical applications
Bibliographic record
Abstract
Journal Article Limitations of large language models in medical applications Get access Jiawen Deng, Jiawen Deng Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario M5S 1A8, Canada Corresponding author. Temerty Faculty of Medicine, University of Toronto, 1 King's College Circle, Toronto, Ontario M5S 1A8, Canada. E-mail: dengj35@mcmaster.ca https://orcid.org/0000-0002-8274-6468 Search for other works by this author on: Oxford Academic Google Scholar Areeba Zubair, Areeba Zubair Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario M5S 1A8, Canada Search for other works by this author on: Oxford Academic Google Scholar Ye-Jean Park Ye-Jean Park Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario M5S 1A8, Canada Search for other works by this author on: Oxford Academic Google Scholar Postgraduate Medical Journal, qgad069, https://doi.org/10.1093/postmj/qgad069 Published: 24 August 2023 Article history Received: 18 July 2023 Revision received: 27 July 2023 Accepted: 03 August 2023 Published: 24 August 2023
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".