Evidence map›Paper›PMID 41049275›Full record

ArticleJournal of hand surgery global online2025

A Comparison of Artificial Intelligence Platforms in the Utility of Answering Frequently Asked Questions About Carpal Tunnel Syndrome: A Cross-Sectional Study.

Calista Stevens, Mehreen Pasha, Dashun Liu, Andrew Block, Anthony Parrino, Craig Rodner

Abstract read
In one paragraph

Article in Journal of hand surgery global online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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.

Calista StevensUniversity of Connecticut School of Medicine, Farmington, CT.
Mehreen PashaUniversity of Connecticut School of Medicine, Farmington, CT.
Dashun LiuDepartment of Public Health Sciences, University of Connecticut, Farmington, CT.
Andrew BlockDepartment of Orthopedic Surgery, University of Connecticut, Farmington, CT.
Anthony ParrinoDepartment of Orthopedic Surgery, University of Connecticut, Farmington, CT.
Craig RodnerDepartment of Orthopedic Surgery, University of Connecticut, Farmington, CT.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The rise of artificial intelligence (AI) in health care comes with increasing concerns about the use and integrity of the information it generates. Chat Generative Pre-Trained Transformer (ChatGPT) 3.5, Google Gemini, and Bing Copilot are free AI chatbot platforms that may be used for answering medical questions and disseminating medical information. Given that carpal tunnel syndrome accounts for 90% of all neuropathies, it is important to understand the accuracy of the information patients may be receiving. The purpose of this study is to determine the use and accuracy of responses generated by ChatGPT, Google Gemini, and Bing Copilot in answering frequently asked questions about carpal tunnel syndrome. Methods: Two independent authors scored responses using the DISCERN tool. DISCERN consists of 15 questions assessing health information on a five-point scale, with total scores ranging from 15 to 75 points. Then, a two-factor analysis of variance was conducted, with scorer and chatbot type as the factors. Results: One-way analysis of variance revealed no significant difference in DISCERN scores among the three chatbots. The chatbots each scored in the "fair" range, with means of 45 for ChatGPT, 48 for Bing Copilot, and 46 for Google Gemini. The average Journal of the American Medical Association score for ChatGPT and Google Gemini surpassed that of Bing Copilot, with averages of 2.3, 2.3, and 1.8, respectively. Conclusions: ChatGPT, Google Gemini, and Bing Copilot platforms generated relatively reliable answers for potential patient questions about carpal tunnel syndrome. However, users should continue to be aware of the shortcomings of the information provided, given the lack of citations, potential for misconstrued information, and perpetuated biases that inherently come with using such platforms. Future studies should explore the response quality for less common orthopedic pathologies and assess patient perceptions of response readability to determine the value of AI as a patient resource across the medical field. Type of study/level of evidence: Cross-sectional study V.

Indexed as

Carpal tunnel syndromeGenerative artificial intelligenceHandOrthopedic surgery

Identifiers

PMID41049275
PMCPMC12489814

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.