用AI智能匹配患者与临床试验，支持动态评估和专家审核。
Toward an AI Reasoning-Enabled System for Patient-Clinical Trial Matching
- 基于大语言模型生成可解释的匹配推理链
- 将匹配状态视为动态过程，可推荐未来可能入组
- 兼顾安全性和可审计性，减轻研究人员负担
临床试验患者筛选仍依赖人工，耗时且资源密集。本文提出一个安全、可扩展的AI增强型患者-试验匹配原型系统，解决异构电子健康记录（EHR）数据整合、专家审核支持及严格安全标准等关键挑战。系统利用开源、具备推理能力的大语言模型，突破传统二分类限制，生成结构化、可解释的准入评估结果，支持人机协同审查。该决策支持工具将匹配状态视为动态过程，不仅识别当前可入组患者，还提供未来可能符合条件的行动建议。系统旨在降低协调员工作负担，智能拓展每名患者可考虑的试验范围，并确保所有AI输出的完整可审计性。
原文摘要 · Abstract (English)
Screening patients for clinical trial eligibility remains a manual, time-consuming, and resource-intensive process. We present a secure, scalable proof-of-concept system for Artificial Intelligence (AI)-augmented patient-trial matching that addresses key implementation challenges: integrating heterogeneous electronic health record (EHR) data, facilitating expert review, and maintaining rigorous security standards. Leveraging open-source, reasoning-enabled large language models (LLMs), the system moves beyond binary classification to generate structured eligibility assessments with interpretable reasoning chains that support human-in-the-loop review. This decision support tool represents eligibility as a dynamic state rather than a fixed determination, identifying matches when available and offering actionable recommendations that could render a patient eligible in the future. The system aims to reduce coordinator burden, intelligently broaden the set of trials considered for each patient and guarantee comprehensive auditability of all AI-generated outputs.
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