ArticleJournal of hand surgery global online2024
Large Language Models in the Diagnosis of Hand and Peripheral Nerve Injuries: An Evaluation of ChatGPT and the Isabel Differential Diagnosis Generator.
Article in Journal of hand surgery global online, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Advances in Artificial Intelligence for Wrist Joint Injury Diagnosis.International journal of medical sciences · 2026Review
- Assessment of brachial plexus and upper-extremity peripheral nerve injuries at the anatomical level using multimodal generative models.Frontiers in surgery · 2026Article
- How Well Does ChatGPT-4o Reason? Expert Evaluation of Diagnostic and Therapeutic Performance in Hand Surgery.Journal of clinical medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Tools using artificial intelligence may help reduce missed or delayed diagnoses and improve patient care in hand surgery. This study aimed to compare and evaluate the performance of two natural language processing programs, Isabel and ChatGPT-4, in diagnosing hand and peripheral nerve injuries from a set of clinical vignettes. Methods: Cases from a virtual library of hand surgery case reports with no history of trauma or previous surgery were included in this study. The clinical details (age, sex, symptoms, signs, and medical history) of 16 hand cases were entered into Isabel and ChatGPT-4 to generate top 10 differential diagnosis lists. Isabel and ChatGPT-4's inclusion and median rank of the correct diagnosis within each list were compared. Two hand surgeons were then provided each list and asked to independently evaluate the performance of the two systems. Results: Isabel correctly identified 7/16 (44%) cases with a median rank of two (interquartile range = 3). ChatGPT-4 correctly identified 14/16 (88%) of cases with a median rank of one (interquartile range = 1). Physicians one and two, respectively, preferred the lists generated by ChatGPT-4 in 12/16 (75%) and 13/16 (81%) of cases and had no preference in 2/16 (13%) cases. Conclusions: ChatGPT-4 had significantly greater diagnostic accuracy within our sample ( Clinical relevance: Despite large language models' potential utility in generating medical diagnoses, physicians must continue to exercise high caution and use their clinical judgment when making diagnostic decisions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.