Evidence map›Paper›PMID 40964819›Full record

ArticleHand (New York, N.Y.)2026

Do ChatGPT and Gemini's Recommendations Align With Established Guidelines for Hand and Upper Extremity Surgery?

Yibin B Zhang, Fielding S Fischer, Matthew V Abola, Daniel A Osei, Scott W Wolfe, Troy B Amen

Abstract read
In one paragraph

Article in Hand (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Yibin B ZhangHarvard Medical School, Boston, MA, USA.ORCID 0000-0003-2546-6790
Fielding S FischerHarvard Medical School, Boston, MA, USA.ORCID 0000-0002-6994-5120
Matthew V AbolaHospital for Special Surgery, New York, NY, USA.ORCID 0000-0001-8084-3515
Daniel A OseiHospital for Special Surgery, New York, NY, USA.
Scott W WolfeHospital for Special Surgery, New York, NY, USA.ORCID 0000-0001-5717-8668
Troy B AmenHospital for Special Surgery, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of large language models (LLMs) such as ChatGPT and Gemini in clinical settings has surged, presenting potential benefits in reducing administrative workload and enhancing patient communication. However, concerns about the clinical accuracy of these tools persist. This study evaluated the concordance of ChatGPT and Gemini's recommendations with American Academy of Orthopedic Surgeons (AAOS) clinical practice guidelines (CPGs) for carpal tunnel syndrome, distal radius fractures, and glenohumeral joint osteoarthritis.

methodsChatGPT (version 4o) and Gemini (version 1.5 Flash) were queried using structured text-based prompts aligned with AAOS CPGs. The LLMs' outputs were analyzed by blinded reviewers to determine concordance with the guidelines. Concordance rates were compared across models, topics, and guideline strength using descriptive statistics and McNemar's test. The transparency of responses, including source citation, was also assessed.

resultsA total of 174 recommendations were generated, with an overall concordance rate of 62.1%. When comparing concordance rates between LLMs, there was no statistically significant difference between ChatGPT and Gemini (66.7% vs 57.5%,

conclusionsDespite modest concordance rates, both models exhibited significant limitations, including variability across topics and guideline strengths, as well as insufficient citation transparency. These findings highlight the challenges in integrating LLMs into clinical practice and emphasize the need for further refinement and evaluation before adoption in hand surgery.

Indexed as

Guideline AdherenceHandOrthopedic ProceduresPractice Guidelines as TopicUpper ExtremityCarpal Tunnel SyndromeGenerative Artificial IntelligenceHumansLarge Language ModelsOsteoarthritisRadius FracturesWrist Fracturesartificial intelligence in healthcareclinical accuracy and transparencyclinical decision-makingCPGs (clinical practice guidelines)LLMs (large language models)

Identifiers

PMID40964819
PMCPMC12446276

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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.