Evidence map›Paper›PMID 39139744›Full record

ArticleOrthopaedic journal of sports medicine2024

Understanding How ChatGPT May Become a Clinical Administrative Tool Through an Investigation on the Ability to Answer Common Patient Questions Concerning Ulnar Collateral Ligament Injuries.

Nathan H Varady, Amy Z Lu, Michael Mazzucco, Joshua S Dines, David W Altchek, Riley J Williams, Kyle N Kunze

Abstract read
In one paragraph

Article in Orthopaedic journal of sports medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
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.

Nathan H VaradyDepartment of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA.
Amy Z LuWeill Cornell Medical College, New York, New York, USA.
Michael MazzuccoWeill Cornell Medical College, New York, New York, USA.
Joshua S DinesDepartment of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA.
David W AltchekWeill Cornell Medical College, New York, New York, USA.
Riley J WilliamsDepartment of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA.
Kyle N KunzeDepartment of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA.

Funding

Weill Cornell/Rockefeller/Sloan Kettering MST ProgramT32GM152349 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI KATHARINE C HSU · 2024 to 2026
$6.6M
NIGMS NIH HHS T32 GM152349
6 · The paper itself

Abstract

Background: The consumer availability and automated response functions of chat generator pretrained transformer (ChatGPT-4), a large language model, poise this application to be utilized for patient health queries and may have a role in serving as an adjunct to minimize administrative and clinical burden. Purpose: To evaluate the ability of ChatGPT-4 to respond to patient inquiries concerning ulnar collateral ligament (UCL) injuries and compare these results with the performance of Google. Study Design: Cross-sectional study. Methods: Google Web Search was used as a benchmark, as it is the most widely used search engine worldwide and the only search engine that generates frequently asked questions (FAQs) when prompted with a query, allowing comparisons through a systematic approach. The query "ulnar collateral ligament reconstruction" was entered into Google, and the top 10 FAQs, answers, and their sources were recorded. ChatGPT-4 was prompted to perform a Google search of FAQs with the same query and to record the sources of answers for comparison. This process was again replicated to obtain 10 new questions requiring numeric instead of open-ended responses. Finally, responses were graded independently for clinical accuracy (grade 0 = inaccurate, grade 1 = somewhat accurate, grade 2 = accurate) by 2 fellowship-trained sports medicine surgeons (D.W.A, J.S.D.) blinded to the search engine and answer source. Results: ChatGPT-4 used a greater proportion of academic sources than Google to provide answers to the top 10 FAQs, although this was not statistically significant (90% vs 50%; Conclusion: ChatGPT-4 is capable of providing responses with clinically relevant content concerning UCL injuries and reconstruction. ChatGPT-4 utilized a greater proportion of academic websites to provide responses to FAQs representative of patient inquiries compared with Google Web Search and provided significantly more accurate answers. Moving forward, ChatGPT has the potential to be used as a clinical adjunct when answering queries about UCL injuries and reconstruction, but further validation is warranted before integrated or autonomous use in clinical settings.

Indexed as

artificial intelligenceelbowlarge language modelpatient informationsportstechnology

Identifiers

PMID39139744
PMCPMC11320692

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.