Systematic review of AI-enhanced oncology MDTs shows mixed concordance and limited cost-effectiveness evidence

Photo by CDC / Unsplash

Frontiers in Medicine Published May 25, 2026 Medically reviewed May 25, 2026 DOI ↗ By Dr. Julia Lee, PhD · Oncology, Genomics & Drug Development

Key Takeaway

Consider AI as strategic augmentation for MDTs, noting limited cost-effectiveness evidence and struggles with nuanced judgment.

This systematic review evaluated 29 peer-reviewed studies regarding AI-enhanced oncology MDT decision-making against human tumor boards. The scope covered clinical efficacy, socioeconomic implications, and specific performance metrics within oncology MDT meetings. The authors synthesized data on concordance rates, guideline adherence, and task-specific capabilities such as molecular target identification. The review also addressed challenges in handling complex, individualized cases and nuanced clinical judgment involving patient-specific factors.

The analysis revealed that concordance rates with human tumor boards ranged from 62% to 76%. AI systems demonstrated notable strengths in standardizing guideline-adherent recommendations and identifying molecular targets. Preparation time was reduced, and access to sub-specialty expertise was described as democratized. However, the review highlighted struggles with nuanced clinical judgment and handling complex, individualized cases.

Regarding socioeconomic implications, the evidence for cost-effectiveness is limited, and rigorous data remains scarce. The authors note that limitations exist in complex scenarios and that nuanced clinical judgment is difficult for AI. The review concludes that AI-Enhanced Oncology MDT 2.0 represents strategic augmentation rather than replacement of human judgment. Practice relevance is restrained by the current lack of robust cost-effectiveness evidence and the need for human oversight in nuanced situations.

Study Details

Study typeMeta analysis

EvidenceLevel 1

PublishedMay 2026

View Original Abstract ↓

Multidisciplinary team (MDT) meetings are the cornerstone of modern oncology, however, meantime face persistent challenges including variability in decision-making, time constraints, and unequal access to subspecialty expertise. The emergence of large language models (LLMs) has catalyzed a new paradigm—AI-Enhanced Oncology MDT 2.0. This systematic review synthesizes evidence from 29 peer-reviewed studies (2020–2026) to evaluate the clinical efficacy and socioeconomic implications of integrating AI into oncology MDT decision-making. Key findings demonstrate that AI systems achieve concordance rates with human tumor boards of 62-76% across multiple cancer types (excluding molecular tumor board studies), with substantial agreement in guideline-driven decisions but limitations in complex, individualized cases. AI demonstrates notable strengths in standardizing guideline-adherent recommendations and supporting molecular target identification, but still struggles with nuanced clinical judgment and patient-specific factors. From a socioeconomic perspective, AI offers potential for value-based care reconstruction through reduced MDT preparation time and democratized access to sub-specialty expertise, though rigorous cost-effectiveness evidence remains limited. The review concludes that AI-Enhanced Oncology MDT 2.0 represents strategic augmentation rather than replacement of human judgment.