ArticleJournal of cancer research and clinical oncology2026
When AI joins the table: evaluating large language model performance in soft tissue sarcoma tumor board decisions.
Article in Journal of cancer research and clinical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Leveraging artificial intelligence to support surgical oncology multidisciplinary team decision-making: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Precision oncology meets Generative AI: assessing large language models in multidisciplinary GIST tumor boards.BMC cancer · 2026Article
- Feasibility and Concordance of a Large Language Model (ChatGPT-5) as a Clinical Decision Support Tool in Gynecologic Oncology Tumor Boards: A Blinded, Multi-Observer Study.Journal of clinical medicine · 2026Article
- Clinical evaluation of large language model recommendations in melanoma: comparison with multidisciplinary tumor board decisions in a real-world cohort.Frontiers in oncology · 2026Article
- AI-enhanced oncology MDT 2.0: from multi-modal data synergy to value-based care reconstruction - a systematic review of clinical efficacy and socioeconomic benefits.Frontiers in oncology · 2026Review
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Authors and funding
14 authors.
Funding
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Abstract
objectivesMultidisciplinary tumor boards (MDTs) are critical for the personalized management of soft tissue sarcomas (STS), but they are limited by time, costs, and resource demands. With recent advances in large language models (LLMs) like ChatGPT, there is growing interest in evaluating their potential role in augmenting MDT workflows. This study aimed to assess the clinical performance of ChatGPT-4o in real-world STS cases using predefined evaluation criteria, comparing its treatment suggestions with expert MDT decisions. MATERIALS AND
methodsThis retrospective study included 152 patients presented to the multidisciplinary sarcoma tumor board. ChatGPT-4o was prompted to generate guideline-based treatment recommendations based on anonymized tumor board registration letters. Outputs were scored by blinded expert reviewers using a five-domain framework: diagnostic modalities, therapeutic modalities, treatment sequencing/timing, chemotherapy regimen, and clinical contextualization. Descriptive statistics and non-parametric ANOVA with post hoc tests assessed performance, including subgroup analysis by sarcoma subtype.
resultsChatGPT-4o scores were significantly lower than the maximum achievable value of 1.0 across all five criteria (all p < 0.0001). Among individual domains, clinical contextualization significantly outperformed all other criteria in pairwise comparisons (all p < 0.05). No significant performance differences were observed across sarcoma subtypes (H = 19.74, p = 0.138).
conclusionsChatGPT-4o demonstrated substantial expert-rated performance in generating tumor board recommendations for soft tissue sarcoma cases, particularly excelling in personalized contextualization. Discrepancies in treatment sequencing and chemotherapy selection highlight the need for expert oversight. These findings support the feasibility of LLM integration into oncology workflows, warranting further refinement toward safe, supportive clinical use.
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