Evidence map›Paper›PMID 41583817›Full record

ArticleArthroscopy, sports medicine, and rehabilitation2025

ChatGPT Can Provide Satisfactory Answers to Assessments of Mock Clinical Photos Following Anterior Cruciate Ligament Reconstruction and Rotator Cuff Repair.

Ian Hollyer, Grant G Schroeder, Bill Young, Connor Sholtis, Anastasia Hunt, Jayme Koltsov, Seth L Sherman, Marc R Safran

Abstract read
In one paragraph

Article in Arthroscopy, sports medicine, and rehabilitation, 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

8 authors.

Ian HollyerDepartment of Orthopaedic Surgery, Stanford University Medical Center, Palo Alto, California, U.S.A.
Grant G SchroederDepartment of Orthopaedic Surgery, Stanford University Medical Center, Palo Alto, California, U.S.A.
Bill YoungDepartment of Orthopaedic Surgery, Stanford University Medical Center, Palo Alto, California, U.S.A.
Connor SholtisDepartment of Orthopaedic Surgery, Stanford University Medical Center, Palo Alto, California, U.S.A.
Anastasia HuntDepartment of Orthopaedic Surgery, Stanford University Medical Center, Palo Alto, California, U.S.A.
Jayme KoltsovDepartment of Orthopaedic Surgery, Stanford University Medical Center, Palo Alto, California, U.S.A.
Seth L ShermanDepartment of Orthopaedic Surgery, Stanford University Medical Center, Palo Alto, California, U.S.A.
Marc R SafranDepartment of Orthopaedic Surgery, Stanford University Medical Center, Palo Alto, California, U.S.A.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To evaluate the ability of ChatGPT-4o to provide clinical assessments of mock photos of knees and shoulders that had undergone arthroscopic or open surgery and answer common questions related to anterior cruciate ligament reconstruction and rotator cuff repair. Methods: The authors created a series of mock postoperative knee and shoulder photos and common questions about anterior cruciate ligament reconstruction and rotator cuff repair. These clinical photos and questions were presented to GPT in isolated chat windows and without prior training, and its responses were graded by orthopaedic surgeons, physician assistants, and residents using a previously established 4-point grading system. Results: When grading ChatGPT's assessments of mock clinical photos, raters gave 18.8% to 75.0% total excellent grades and 0% to 25.0% unsatisfactory grades. When grading ChatGPT question answers, raters gave 4.5% to 77.3% total excellent grades and 0% to 9.1% unsatisfactory grades. Mean grades ranged from excellent to satisfactory, requiring moderate clarification, depending on the photo or question. There was wide variability in rater grading agreement, with attendings being the most critical graders. Conclusions: ChatGPT-4o can provide some satisfactory assessments of simple postoperative clinical photos and responses to common questions after orthopaedic knee and shoulder surgery, as graded by orthopaedic surgeons, residents, and physician assistants. While this study highlights how ChatGPT could serve as a tool for generating draft responses to patient concerns after undergoing orthopaedic knee and shoulder surgery, it cannot yet provide reliable, independent clinical advice. Clinical Relevance: As digital interactions in health care continue to expand, large language models could play a role in enhancing communication between patients and the orthopaedic care team, potentially improving patient engagement and access to information.

Identifiers

PMID41583817
PMCPMC12827220

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.