Evidence map›Paper›PMID 42081743›Full record

ArticleJMIR perioperative medicine2026

Physician Perspectives on ChatGPT-4o as a Patient Resource for Abdominal Cancer Surgeries: Cross-Sectional Survey.

Christina V Lindsay, Devika A Shenoy, Allison N Martin, Christie L Clipper, Kevin N Shah, Michael E Lidsky, Daniel P Nussbaum, Ralph Snyderman

Abstract read
In one paragraph

Article in JMIR perioperative medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

8 authors.

Christina V LindsaySchool of Medicine, Duke University, 8 Searle Center Dr, Durham, NC, 27710, United States, 1 305-439-7523.ORCID 0000-0001-6421-0996
Devika A ShenoySchool of Medicine, Duke University, 8 Searle Center Dr, Durham, NC, 27710, United States, 1 305-439-7523.ORCID 0009-0006-7543-5414
Allison N MartinDepartment of Surgery, Duke University, Durham, NC, United States.ORCID 0000-0002-9216-0647
Christie L ClipperCenter for Personalized Health Care, Duke University, Durham, NC, United States.ORCID 0000-0002-6566-3548
Kevin N ShahDepartment of Surgery, Duke University, Durham, NC, United States.ORCID 0000-0002-8466-4001
Michael E LidskyDepartment of Surgery, Duke University, Durham, NC, United States.ORCID 0000-0003-0500-2016
Daniel P NussbaumDepartment of Surgery, Duke University, Durham, NC, United States.ORCID 0000-0003-3070-6605
Ralph SnydermanCenter for Personalized Health Care, Duke University, Durham, NC, United States.ORCID 0009-0003-5252-5036

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) models are being increasingly integrated into clinical care. Moreover, the availability of publicly accessible AI resources makes them attractive to patients seeking clinical information. Little is known regarding the use of large language models as patient resources for navigating major cancer diagnoses. Objective: This study aimed to evaluate the content, readability, and safety of ChatGPT (OpenAI; GPT-4o)-generated responses to common perioperative queries about hepatic, pancreatic, and colon cancers. Methods: A 28-question survey was developed based on frequently asked surgical questions for select malignancies. Surgical oncologists rated ChatGPT-4o-generated responses on a 5-point Likert scale for accuracy, quality, and tangibility. Readability was assessed using the Flesch-Kincaid Reading Grade Level (FKRGL) and Flesch Reading Ease (FRE). Respondents provided free-text comments and reported their comfort with patients using ChatGPT. Survey completion implied consent. Results: A total of 7 attending surgical oncologists with a median of 7 (IQR 4-13) years in practice completed the survey. Responses received mean scores of 3.5/5 (SD 0.28) for quality, 3.6/5 (SD 0.34) for accuracy, and 3.6/5 (SD 0.29) for tangibility. The responses had a median FKRGL score of 14.6 (IQR 13.3-15.6) and FRE score of 29.4 (IQR 20.5-36.3). On a post hoc analysis for select questions, the median FKRGL was 15.6 (IQR 14.4-16.7), decreasing to 7.1 (IQR 6.1-8.3) and 14.5 (IQR 13.2-15.4) with prompting and rephrasing, and the median FRE was 18.1 (IQR 14.6-24.7), increasing to 73.8 (IQR 66.6-79.3) and 32.0 (IQR 27.0-37.7) with prompting and rephrasing. Numerous inaccuracies and content gaps were reported, and approximately 43% (3/7) of providers did not report feeling "comfortable" in having patients consult publicly available AI for medical information. Conclusions: This study provides cautionary, yet optimistic, findings regarding the value of publicly accessible ChatGPT as a patient resource for abdominal malignancies. Providers should be prepared to effectively counsel patients to identify their educational attainment level when using ChatGPT to mitigate readability challenges.

Indexed as

Attitude of Health PersonnelPhysiciansCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansLarge Language ModelsMaleMiddle AgedSurveys and Questionnairesgenerative artificial intelligencehealth literacypatient educationperioperative caresurgical oncology

Identifiers

PMID42081743
PMCPMC13138707

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Registered trials

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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.