Leveraging artificial intelligence to support surgical oncology multidisciplinary team decision-making: a systematic review
IntroductionArtificial intelligence (AI)–based clinical decision support systems (CDSSs) are increasingly used to support clinicians by providing evidence-based treatment recommendations for multidisciplinary team (MDT) meetings. This systematic review mapped the current landscape of AI-based...
IntroductionArtificial intelligence (AI)–based clinical decision support systems (CDSSs) are increasingly used to support clinicians by providing evidence-based treatment recommendations for multidisciplinary team (MDT) meetings. This systematic review mapped the current landscape of AI-based CDSSs in surgical oncology decision-making and synthesized evidence on their performance and factors influencing effectiveness.MethodsCochrane, Ovid MEDLINE, and Embase were searched on February 3, 2025. Studies evaluating AI-based CDSSs for therapeutic decision-making in surgical oncology were included. Methodological quality was assessed using the Critical Appraisal Skills Programme Diagnostic Study Checklist, and data were synthesized narratively.ResultsFifty-nine studies, encompassing 23,158 patients, were included. CDSSs were classified into five categories: decision tree–based systems, knowledge representation–based systems, Watson for Oncology (WfO), large language models, and other AI-based systems. Concordance with MDT or guideline-based recommendations ranged from 23.2% to 99%. Decision tree– and knowledge-based systems generally demonstrated higher concordance and improved guideline adherence, while WfO performance varied substantially by region and treatment accessibility. Three key themes related to implementation challenges emerged: technical limitations, socioeconomic and healthcare system constraints, and patient- and tumour-specific factors. Reported benefits included improved adherence of MDT decisions to clinical guidelines, enhanced identification of patients eligible for clinical trial enrolment, support for less experienced clinicians, and facilitation of triage for routine cases.DiscussionAI-based CDSSs show promise in supporting MDT decision-making but remain constrained by challenges related to system maintenance, variability in clinical protocols, therapeutic availability, and patient heterogeneity. Larger prospective studies are needed to evaluate the real-world integration, clinical impact, and patient outcomes of AI-based CDSSs within MDT workflows.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42025639227, identifer: CRD42025639227.Source: Frontiers AI — Published — Category: Research