Evidence map›Paper›PMID 42402154›Full record

ArticleJournal of surgical oncology2026

Evaluating the Accuracy of ChatGPT-4o in Addressing Complex Clinical Questions Based on NCCN Guidelines for Rectal Adenocarcinoma.

Ryan Meyer, Tamir E Bresler, Kevin M Palmer, Tyler Wilson, Shivam Pandya, Manabu Fujita

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Article in Journal of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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.

Ryan MeyerDepartment of Surgery, Los Robles Regional Medical Center, Thousand Oaks, California, USA.ORCID https://orcid.org/0000-0001-5847-9577
Tamir E BreslerDepartment of Surgery, Los Robles Regional Medical Center, Thousand Oaks, California, USA.ORCID https://orcid.org/0000-0001-8397-9186
Kevin M PalmerDepartment of Surgery, Los Robles Regional Medical Center, Thousand Oaks, California, USA.ORCID https://orcid.org/0009-0004-8353-7598
Tyler WilsonDepartment of Surgery, Los Robles Regional Medical Center, Thousand Oaks, California, USA.ORCID https://orcid.org/0009-0002-7992-3328
Shivam PandyaDepartment of Surgery, Los Robles Regional Medical Center, Thousand Oaks, California, USA.
Manabu FujitaDepartment of Surgery, Los Robles Regional Medical Center, Thousand Oaks, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe management of rectal adenocarcinoma requires navigation of complex, branching guideline pathways encompassing neoadjuvant sequencing, surgical approach, organ preservation, and surveillance, yet real-world guideline adherence remains as low as 60-70%. The ability of current-generation large language models (LLMs) to accurately navigate these decision points has not been fully characterized.

methodsIn this cross-sectional, vignette-based study, 135 clinical questions were constructed from 45 pages of NCCN Rectal Cancer Guidelines (Version 4.2024). ChatGPT-4o was queried using standardized prompts with up to 3 clarifying questions permitted per query. Responses were independently evaluated by two physician raters on a 5-point Likert scale, with potential discrepancies adjudicated by a board-certified surgical oncologist. Primary outcomes were the proportion of responses rated Correct (score ≥ 3) and Accurate (score ≥ 4). Inter-rater reliability was assessed using Cohen's kappa, and subgroup analysis was performed across clinical domains using the Kruskal-Wallis test.

resultsOf 135 questions, 127 (94.1%; 95% CI, 88.7-97.0%) were Correct and 121 (89.6%; 95% CI, 83.3-93.7%) were Accurate. One hundred two responses (75.6%) were completely correct without additional prompting. Performance was consistent across clinical domains (Kruskal-Wallis H = 0.530, p = 0.767). Inter-rater agreement was perfect (κ = 1.0). Eight responses (5.9%) contained partially or wholly incorrect information, with errors concentrated in multi-step conditional treatment decision points.

conclusionChatGPT-4o demonstrates high concordance with NCCN rectal cancer guidelines across all evaluated clinical domains with notable improvement over prior ChatGPT iterations evaluated by our group. The concentration of errors in complex conditional treatment algorithms suggests that LLMs excel at discrete factual recall but may struggle with multi-step reasoning under clinical uncertainty. Prospective validation using real-world clinical data and comparison with multidisciplinary tumor board recommendations remain necessary prior to clinical integration.

Indexed as

AdenocarcinomaGuideline AdherencePractice Guidelines as TopicRectal NeoplasmsCross-Sectional StudiesGenerative Artificial IntelligenceHumansLarge Language ModelsReproducibility of Resultsartificial intelligenceclinical decision supportlarge language modelsNCCN guidelinesrectal cancer

Identifiers

PMID42402154
PMCPMC13575598

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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.