Evidence map›Paper›PMID 39534040›Full record

ArticleArthroscopy, sports medicine, and rehabilitation2024

ChatGPT and Google Provide Mostly Excellent or Satisfactory Responses to the Most Frequently Asked Patient Questions Related to Rotator Cuff Repair.

Martinus Megalla, Alexander K Hahn, Jordan A Bauer, Jordan T Windsor, Zachary T Grace, Marissa A Gedman, Robert A Arciero

Abstract read
In one paragraph

Article in Arthroscopy, sports medicine, and rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

7 authors.

Martinus MegallaUniversity of Connecticut, Farmington, Connecticut, U.S.A.
Alexander K HahnUniversity of Connecticut, Farmington, Connecticut, U.S.A.
Jordan A BauerUniversity of Connecticut, Farmington, Connecticut, U.S.A.
Jordan T WindsorHackensack Meridian School of Medicine, Nutley, New Jersey, U.S.A.
Zachary T GraceUniversity of Connecticut, Farmington, Connecticut, U.S.A.
Marissa A GedmanUniversity of Connecticut, Farmington, Connecticut, U.S.A.
Robert A ArcieroUniversity of Connecticut, Farmington, Connecticut, U.S.A.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To assess the differences in frequently asked questions (FAQs) and responses related to rotator cuff surgery between Google and ChatGPT. Methods: Both Google and ChatGPT (version 3.5) were queried for the top 10 FAQs using the search term "rotator cuff repair." Questions were categorized according to Rothwell's classification. In addition to questions and answers for each website, the source that the answer was pulled from was noted and assigned a category (academic, medical practice, etc). Responses were also graded as "excellent response not requiring clarification" (1), "satisfactory requiring minimal clarification" (2), "satisfactory requiring moderate clarification" (3), or "unsatisfactory requiring substantial clarification" (4). Results: Overall, 30% of questions were similar between what Google and ChatGPT deemed to be the most FAQs. For questions from Google web search, most answers came from medical practices (40%). For ChatGPT, most answers were provided by academic sources (90%). For numerical questions, ChatGPT and Google provided similar responses for 30% of questions. For most of the questions, both Google and ChatGPT responses were either "excellent" or "satisfactory requiring minimal clarification." Google had 1 response rated as satisfactory requiring moderate clarification, whereas ChatGPT had 2 responses rated as unsatisfactory. Conclusions: Both Google and ChatGPT offer mostly excellent or satisfactory responses to the most FAQs regarding rotator cuff repair. However, ChatGPT may provide inaccurate or even fabricated answers and associated citations. Clinical Relevance: In general, the quality of online medical content is low. As artificial intelligence develops and becomes more widely used, it is important to assess the quality of the information patients are receiving from this technology.

Identifiers

PMID39534040
PMCPMC11551354

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.