Evidence map›Paper›PMID 42681963›Full record

ArticleCancer medicine2026

Agreement in Staging and Treatment Recommendations Among Clinicians, Text-Only Clinicians, DeepSeek-V3, and ChatGPT-4o for Nasopharyngeal Carcinoma Patients.

Shenglan Lin, Cai Zhang, Feifei Zhong, Xiyi Liao, Liuyun Gong, Dunhuang Wang

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Article in Cancer 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.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shenglan LinDepartment of Gerontology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.ORCID https://orcid.org/0009-0002-2651-6833
Cai ZhangDepartment of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Feifei ZhongDepartment of Medical Education, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Xiyi LiaoDepartment of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Liuyun GongDepartment of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.ORCID https://orcid.org/0000-0002-6795-8235
Dunhuang WangDepartment of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.ORCID https://orcid.org/0000-0002-9253-3650

Funding

The 2024 Youth Research Project of Xiamen Health High-Quality Development Science and Technology Plan 2024GZL-QN032The 2025 Xiamen Healthcare Guidance Projects 3502Z2025ZD1021
6 · The paper itself

Abstract

purposeWith AI tools being increasingly utilized for medical inquiries, this study evaluated the agreement between clinicians, text-only clinicians, DeepSeek-V3, and ChatGPT-4o in TNM staging and preferred treatment recommendations for newly diagnosed nasopharyngeal carcinoma (NPC) patients.

methodsA retrospective study analyzed 322 consecutive NPC patients treated at our institution from January 2023 to February 2025. TNM staging (AJCC 8th edition) and preferred treatment recommendations were independently assessed by text-only clinicians, DeepSeek-V3, and ChatGPT-4o. Interrater agreement was quantified using Cohen's kappa coefficient and Fleiss' kappa analysis (κ), with the range of κ = 0.81-1.00 considered almost perfect agreement.

resultsThe cohort comprised 244 males and 78 females (median age, 52 years, range, 18-77 years). Cohen's kappa analysis showed that for clinical staging, moderate agreement was exhibited in clinician-AI comparisons, while substantial agreement was observed between clinicians and text-only clinicians. For preferred treatment recommendations, slight agreement was noted in clinician-AI comparisons, while moderate agreement was found between clinicians and text-only clinicians. Fleiss' kappa analysis demonstrated moderate agreement among the 4 raters for T stage, N stage, and clinical staging, while M stage achieved almost perfect agreement. However, overall agreement for treatment recommendations was fair.

conclusionsThe AI tools demonstrated moderate agreement with clinicians in overall clinical staging for NPC, with heterogeneous performance across T, N, and M categories (almost perfect for M stage, moderate for T and N stages), whereas their preferred treatment recommendations showed only slight agreement with clinical decision-making.

Indexed as

Nasopharyngeal CarcinomaNasopharyngeal NeoplasmsAdolescentAdultAgedFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedNeoplasm StagingRetrospective StudiesYoung Adultartificial intelligenceChatGPT‐4oDeepSeek‐V3nasopharyngeal carcinoma

Identifiers

PMID42681963
PMCPMC13535144

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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.