演示：用于分子肿瘤委员会患者摘要的医疗智能体编排器（HAO）
Demo: Healthcare Agent Orchestrator (HAO) for Patient Summarization in Molecular Tumor Boards
AI总结：
针对分子肿瘤委员会患者摘要编制耗时易漏的问题，提出LLM驱动的多智能体编排器HAO及“模型即法官”评估框架TBFact，实现高信息捕获率与本地化无数据评估。
摘要：
分子肿瘤委员会（MTBs）是由肿瘤学专家组成的多学科论坛，专家们在此协作评估复杂的患者病例，以确定最佳治疗策略。该过程的核心要素是患者摘要，通常由肿瘤内科医生、放射肿瘤科医生或外科医生，或其经过培训的医疗助理编制，他们将异质的医疗记录提炼为简洁的叙述以促进讨论。这种人工方法往往劳动密集、主观性强，且容易遗漏关键信息。为解决这些局限性，我们引入了医疗智能体编排器（HAO），一种由大型语言模型（LLM）驱动的AI智能体，它协调多智能体临床工作流，为MTBs生成准确且全面的患者摘要。由于风格差异、顺序、同义词使用和措辞差异，将预测的患者摘要与真实值进行比较会带来额外挑战，这使简洁性和完整性的测量变得复杂。为克服这些评估障碍，我们提出了TBFact，一个“模型即法官”框架，旨在评估生成摘要的全面性和简洁性。利用源自去标识化肿瘤委员会讨论的基准数据集，我们应用TBFact来评估我们的患者历史智能体。结果表明，该智能体捕获了94%的高重要性信息（包括部分蕴含），并在严格蕴含标准下实现了0.84的TBFact召回率。我们进一步证明，TBFact支持一种无数据评估框架，机构可在本地部署而无需共享敏感临床数据。HAO和TBFact共同为向MTBs提供可靠且可扩展的支持奠定了坚实基础。
英文摘要：
Molecular Tumor Boards (MTBs) are multidisciplinary forums where oncology specialists collaboratively assess complex patient cases to determine optimal treatment strategies. A central element of this process is the patient summary, typically compiled by a medical oncologist, radiation oncologist, or surgeon, or their trained medical assistant, who distills heterogeneous medical records into a concise narrative to facilitate discussion. This manual approach is often labor-intensive, subjective, and prone to omissions of critical information. To address these limitations, we introduce the Healthcare Agent Orchestrator (HAO), a Large Language Model (LLM)-driven AI agent that coordinates a multi-agent clinical workflow to generate accurate and comprehensive patient summaries for MTBs. Evaluating predicted patient summaries against ground truth presents additional challenges due to stylistic variation, ordering, synonym usage, and phrasing differences, which complicate the measurement of both succinctness and completeness. To overcome these evaluation hurdles, we propose TBFact, a ``model-as-a-judge'' framework designed to assess the comprehensiveness and succinctness of generated summaries. Using a benchmark dataset derived from de-identified tumor board discussions, we applied TBFact to evaluate our Patient History agent. Results show that the agent captured 94% of high-importance information (including partial entailments) and achieved a TBFact recall of 0.84 under strict entailment criteria. We further demonstrate that TBFact enables a data-free evaluation framework that institutions can deploy locally without sharing sensitive clinical data. Together, HAO and TBFact establish a robust foundation for delivering reliable and scalable support to MTBs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
