Evidence map›Paper›PMID 40682765›Full record

ArticleInternational journal of cancer2025

Evaluating ChatGPT's recommendations for systematic treatment decisions in recurrent or metastatic head and neck squamous cell carcinoma: Perspectives from experts and junior doctors.

Danfang Yan, Lihong Wang, Liming Huang, Kejia Cheng, Yu Huang, Yangyang Bao, Xin Yin, Mengye He, Huiyong Zhu, Senxiang Yan

Abstract read
In one paragraph

Article in International journal of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Danfang YanDepartment of Radiation Oncology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, Hangzhou, China.
Lihong WangDepartment of Radiation Oncology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, Hangzhou, China.
Liming HuangDepartment of Oncology, The Affiliated People's Hospital, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
Kejia ChengDepartment of Otolaryngology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, Hangzhou, China.
Yu HuangDepartment of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yangyang BaoDepartment of Otolaryngology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, Hangzhou, China.
Xin YinDepartment of Radiation Oncology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, Hangzhou, China.
Mengye HeDepartment of Oncology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, Hangzhou, China.
Huiyong ZhuDepartment of Oral and Maxillofacial Surgery, the First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, Hangzhou, China.
Senxiang YanDepartment of Radiation Oncology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, Hangzhou, China.ORCID 0000-0001-7753-6143

Funding

National Natural Science Foundation of China 62307031
6 · The paper itself

Abstract

This study evaluates ChatGPT-4's potential as a decision-support tool in the treatment of recurrent or metastatic head and neck squamous cell carcinoma (HNSCC). The study involved 12 retrospectively chosen patients with detailed clinical, tumor, treatment history, imaging, pathology, and symptomatic data. ChatGPT-4, along with six experts and 10 junior oncologists, assessed these cases. The AI model applied the 8th edition AJCC TNM criteria for tumor staging and proposed treatment strategies. Performance was quantitatively rated on a 0-100 scale by both expert and junior oncologists, with further analysis through statistical scoring and intraclass correlation coefficients. Findings revealed that ChatGPT-4 achieved an 83.3% accuracy rate in tumor staging with two instances of mis-staging. Junior doctors rated its staging performance highly, showing strong consensus on language capabilities and moderate on learning assistance. Experts rated ChatGPT-4's treatment strategy: high agreement on subject knowledge (median 86, mean 84.7), logical reasoning (median 83, mean 82), and analytical skills (median 85, mean 82); moderate on ChatGPT-4's usefulness for treatment decision (median 80, mean 77) and its recommendations (median 80, mean 76.8). Junior doctors rated ChatGPT-4 higher in treatment strategy (medians above 85) with limited consensus (subject knowledge: median 88, mean 84.5; logical reasoning: median 90, mean 83.2; analytical skills: median 90, mean 82.5; usefulness: median 85, mean 81.8; agreements for: median 85, mean 80.4). ChatGPT is proficient in tumor staging but moderately effective in treatment recommendations. Nonetheless, it shows promise as a supportive tool for clinicians, particularly for those with less experience, in making informed treatment decisions.

Indexed as

Clinical Decision-MakingDecision Support TechniquesHead and Neck NeoplasmsNeoplasm Recurrence, LocalSquamous Cell Carcinoma of Head and NeckAdultAgedFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedNeoplasm MetastasisNeoplasm StagingRetrospective Studiesartificial intelligenceChatGPThead and neck squamous cell carcinomamultidisciplinary teams

Identifiers

PMID40682765
PMCPMC12407031

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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.