ArticleThe oncologist2025
Large language model processing capabilities of ChatGPT 4.0 to generate molecular tumor board recommendations-a critical evaluation on real world data.
Article in The oncologist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Applications of large language models in tumor boards: a systematic review.Oncology reviews · 2026Pooled it
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Article
- Role prompting modulates linguistic style but not clinical decision structure in GPT-5 tumour board simulation.NPJ digital medicine · 2026Article
- Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- Machine learning for chemotherapy decision-making in breast cancer using large language model.Frontiers in digital health · 2026Article
- Artificial intelligence in colorectal cancer multidisciplinary decision-making: concordance, predictive support and clinical translation.Frontiers in oncology · 2026Review
- Large language models in patient education for brain tumors: opportunities, risks, and ethical considerations.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge language models (LLMs) like ChatGPT 4.0 hold promise for enhancing clinical decision-making in precision oncology, particularly within molecular tumor boards (MTBs). This study assesses ChatGPT 4.0's performance in generating therapy recommendations for complex real-world cancer cases compared to expert human MTB (hMTB) teams.
methodsWe retrospectively analyzed 20 anonymized MTB cases from the Comprehensive Cancer Center Augsburg (CCCA), covering breast cancer (n = 3), glioblastoma (n = 3), colorectal cancer (n = 2), and rare tumors. ChatGPT 4.0 recommendations were evaluated against hMTB outputs using metrics including recommendation type (therapeutic/diagnostic), information density (IDM), consistency, quality (level of evidence [LoE]), and efficiency. Each case was prompted thrice to evaluate variability (Fleiss' Kappa).
resultsChatGPT 4.0 generated more therapeutic recommendations per case than hMTB (median 3 vs 1, P = .005), with comparable diagnostic suggestions (median 1 vs 2, P = .501). Therapeutic scope from ChatGPT 4.0 included off-label and clinical trial options. IDM scores indicated similar content depth between ChatGPT 4.0 (median 0.67) and hMTB (median 0.75; P = .084). Moderate consistency was observed across replicate runs (median Fleiss' Kappa = 0.51). ChatGPT 4.0 occasionally utilized lower-level or preclinical evidence more frequently (P = .0019). Efficiency favored ChatGPT 4.0 significantly (median 15.2 vs 34.7 minutes; P < .001).
conclusionIncorporating ChatGPT 4.0 into MTB workflows enhances efficiency and provides relevant recommendations, especially in guideline-supported cases. However, variability in evidence prioritization highlights the need for ongoing human oversight. A hybrid approach, integrating human expertise with LLM support, may optimize precision oncology decision-making.
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