Artificial Intelligence and Multidisciplinary Oncology Decision-Making: A Systematic Review of LLM Concordance with MDT Recommendations and Implications for Clinical Reasoning Education
Abstract
Introduction: Multidisciplinary team (MDT) decision-making is a cornerstone of oncology practice and an important context for developing clinical reasoning skills. Artificial intelligence (AI), particularly large language models (LLMs), has recently been explored as a potential tool to support clinical decision-making in oncology settings.
Methods: This systematic review, conducted according to PRISMA 2020 guidelines, synthesizes evidence on the concordance between LLM-generated recommendations and MDT decisions in oncology. Twenty-two studies were included, primarily comprising retrospective analyses comparing AI-generated outputs with expert MDT consensus across diagnostic, therapeutic, and workflow-related tasks.
Results: LLMs demonstrated generally moderate to high concordance with MDT decisions in guideline-constrained clinical scenarios. However, performance decreased in complex or less-structured cases. Variability in outcomes was associated with model architecture, prompting strategies, and retrieval-augmented approaches. Across studies, AI performance was most consistent when aligned with established clinical guidelines. Substantial heterogeneity existed among models and study designs, limiting direct comparability.
Conclusion: From a cognitive perspective, LLMs may be conceptualized as external information-processing tools that partially mirror structured aspects of MDT reasoning in specific contexts. However, evidence remains limited to retrospective concordance…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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