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<p>Multidisciplinary tumor boards (MDTs) are central to cancer care but remain constrained by scarce experts and variable decision quality. EvoMDT employs a self-evolution loop that updates prompts, consensus weights, and retrieval scope based on expert feedback and outcome signals, improving robustness without sacrificing traceability. This matters clinically because MDT workloads and evidence shift over time, requiring adaptive yet auditable decision support. Agents perform domain-specific inference over lesion-level clinical data with structured knowledge retrieval; a consensus protocol resolves conflicts and generates traceable, evidence-linked recommendations. Evaluation spanned six public oncology QA benchmarks and four real-world datasets (breast, liver, lung, lymphoma), followed by single-blind physician assessment. Quantitative metrics (ROUGE, BERTScore) and automated safety checks assessed factuality and guideline concordance, while clinicians rated clinical appropriateness and usability. EvoMDT outperformed frontier Large Language Models (LLMs) baselines (e.g., Llama-3-70B, Claude-3, Med-PaLM 2), improving guideline concordance and semantic alignment with expert plans (BERTScore 0.62–0.68) and reducing safety violations. In physician review, EvoMDT achieved decision quality comparable to human MDTs while shortening response time by 30–40%. These results position EvoMDT as an interpretable, evidence-traceable framework that operationalizes AI reasoning for multidisciplinary oncology practice and offers a scalable foundation for trustworthy, lesion-level precision cancer care.</p>

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EvoMDT: a self-evolving multi-agent system for structured clinical decision-making in multi-cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Qicai Liu,
  • Zhichao Hu,
  • Tao Huang,
  • Yupeng Niu,
  • Xinche Zhang,
  • Shanwu Ma,
  • Chutong Lin,
  • Goh Kim Huat,
  • Hyeokkoo Eric Kwon,
  • Feng Gao,
  • Xianfu Sun,
  • Zhitao Ying,
  • Guangliang Qiang

摘要

Multidisciplinary tumor boards (MDTs) are central to cancer care but remain constrained by scarce experts and variable decision quality. EvoMDT employs a self-evolution loop that updates prompts, consensus weights, and retrieval scope based on expert feedback and outcome signals, improving robustness without sacrificing traceability. This matters clinically because MDT workloads and evidence shift over time, requiring adaptive yet auditable decision support. Agents perform domain-specific inference over lesion-level clinical data with structured knowledge retrieval; a consensus protocol resolves conflicts and generates traceable, evidence-linked recommendations. Evaluation spanned six public oncology QA benchmarks and four real-world datasets (breast, liver, lung, lymphoma), followed by single-blind physician assessment. Quantitative metrics (ROUGE, BERTScore) and automated safety checks assessed factuality and guideline concordance, while clinicians rated clinical appropriateness and usability. EvoMDT outperformed frontier Large Language Models (LLMs) baselines (e.g., Llama-3-70B, Claude-3, Med-PaLM 2), improving guideline concordance and semantic alignment with expert plans (BERTScore 0.62–0.68) and reducing safety violations. In physician review, EvoMDT achieved decision quality comparable to human MDTs while shortening response time by 30–40%. These results position EvoMDT as an interpretable, evidence-traceable framework that operationalizes AI reasoning for multidisciplinary oncology practice and offers a scalable foundation for trustworthy, lesion-level precision cancer care.