Artificial intelligence in multidisciplinary tumor boards enhancing decision making and clinical outcomes in oncology

Xiaodong Wang; Qianqian Wang; Gouping Ding; Junjie Wang; Yixuan Tang; Yeqian Feng

doi:10.1016/j.isci.2025.114082

Artificial intelligence in multidisciplinary tumor boards enhancing decision making and clinical outcomes in oncology

iScience. 2025 Nov 17;28(12):114082. doi: 10.1016/j.isci.2025.114082. eCollection 2025 Dec 19.

Authors

Xiaodong Wang¹, Qianqian Wang², Gouping Ding¹, Junjie Wang¹, Yixuan Tang², Yeqian Feng¹

Affiliations

¹ Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China.
² Department of Oncology, Zhuzhou Hospital Affiliated to Xiangya School of Medicine, Central South University, Zhuzhou, China.

Abstract

Multidisciplinary tumor boards (MDTs) coordinate complex oncology decisions across imaging, pathology, genomics, and patient factors. Here we synthesize how artificial intelligence (AI)-including machine learning, natural language processing, deep learning, and large language models-supports MDT preparation and deliberation. Across cancers, reported agreement between AI recommendations and MDT decisions commonly ranges from 70% to 90%, and task-focused tools achieve high diagnostic performance in screening and staging. Benefits include faster information synthesis, more consistent guideline alignment, and clearer documentation of options, while human review remains central. Key limitations-data bias, uneven generalizability, privacy and governance concerns, and limited prospective validation-temper adoption. We outline implementation priorities: prospective multicenter evaluation, integration with electronic records, clinician training, and transparent oversight. Overall, AI can augment MDT decision-making and help personalize care and workflow efficiency when deployed with rigorous evaluation and safeguards.

Keywords: Health sciences; Medical informatics.

Publication types

Review