Multi-Agent Consensus Matrix Modeling for Medical Decision-Making: A Role-Specialized LLM Framework for Oncology MDT Consultations

Multidisciplinary team (MDT) consultations are the gold standard for cancer care decision-making, yet current practice lacks structured mechanisms for quantifying consensus and ensuring decision traceability. We introduce a Multi-Agent Medical Decision Consensus Matrix System that deploys seven specialised large language model agents, including an oncologist, a radiologist, a nurse, a psychologist, a patient advocate, a nutritionist and a rehabilitation therapist, to simulate realistic MDT workflows. The framework incorporates a mathematically grounded consensus matrix that uses Kendall’s coefficient of concordance to objectively assess agreement. To further enhance treatment recommendation quality and consensus efficiency, the system integrates reinforcement learning methods, including Q-Learning, PPO, and DQN. Evaluation across five medical benchmarks (MedQA, PubMedQA, DDXPlus, MedBullets, SymCat) shows substantial gains over existing approaches, achieving 87.5% accuracy (83.8% for the strongest baseline), an 89.3% consensus rate, and a mean Kendall’s $W$ of 0.823. Expert reviewers rated the clinical appropriateness of system outputs at 8.9/10. The system guarantees full evidence traceability through mandatory citations of clinical guidelines and peer-reviewed literature following GRADE principles. This work advances medical AI by providing structured consensus measurement, role-specialised multi-agent collaboration, and evidence-based explainability to improve the quality and efficiency of clinical decision-making.