AI-Tumorboard-Data

Engineering notes

Engineering notes will be added by the aipentium editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

Background: Multidisciplinary tumor boards (MTBs) are central to contemporary head and neck oncology, ensuring accurate staging, guideline-concordant therapy, and balanced functional outcomes. In parallel, large language models (LLMs) have demonstrated increasing competence in synthesizing complex clinical data and generating structured recommendations. Their potential role as decision-support tools in head and neck oncology, however, remains insufficiently evaluated.Methods: We retrospectively compared treatment recommendations generated by two state-of-the-art LLMs (ChatGPT ™ and Google Gemini ™) with consensus decisions from a multidisciplinary tumor board. Fifty consecutive, synthetic head and neck cancer cases discussed between April and June 2025 were included without restriction on tumor type or stage. Both models received identical anonymized clinical, radiologic, and histopathologic reports and were prompted to generate guideline-based first- and second-line treatment recommendations

Links and sources

Comments