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PHENO-RAG:一种人工智能工具,用于在肝细胞癌中根据指导方针做出管理决策

Ciro Celsa¹, Mauro Giuffrè², Gabriele Di Maria³

¹Gastroenterology and Hepatology Unit, Department of Health Promotion, Mother & Child Care, Internal Medicine & Medical Specialties, University of Palermo, Palermo, Italy; Department of Surgery & Cancer, Imperial College London, London, UK.

JHEP reports : innovation in hepatology

|March 14, 2026

概括

一个新的AI框架,PHENO-RAG,通过将指南与患者数据相结合,准确地支持肝细胞癌 (HCC) 治疗决策. 该工具有助于分层患者进行多学科团队 (MDT) 讨论,提高护理的一致性.

科学领域:

在瘤学中使用人工智能
肝细胞癌管理肝细胞癌管理
临床决策支持系统临床决策支持系统

背景情况:

由于慢性肝病和多种治疗选择,肝细胞癌 (HCC) 的管理是复杂的.
多学科团队 (MDT) 管理提高了结果,但资源密集.
需要有效的患者分层工具来优化MDT讨论.

研究的目的:

开发和评估PHENO-RAG,一个新的大型语言模型 (LLM) 框架用于HCC患者分层.
整合当代的HCC管理指南与患者特定的临床数据进行决策支持.
评估基于LLM的表型和决策生成与现实世界的临床决策的准确性.

主要方法:

追溯分析了424名HCC患者的489份临床报告.
使用混合REGEX+LLM管道对8个本地托管的LLM (Llama-3,GPT-oss,Qwen,Falcon) 的评估.
配置的评估包括零射击/少数射击学习,结构化/非结构化笔记和检索增强生成 (RAG).

主要成果:

PHENO-RAG框架,特别是GPT-oss-120B与RAG,实现了高精度:治疗分配的86.5%,临床复杂性的88.6%.
通过结构化输入,少量射击示例和RAG提高了性能,超过了基线配置.
MDT的推仍然具有挑战性,表明其在优先考虑中的作用,而不是完全自动化.

结论:

PHENO-RAG提供准确的,符合指导方针的决策支持,用于从现实世界的临床笔记中管理HCC.
框架的性能取决于混合提取,输入结构,上下文示例和证据检索.
为了更广泛的临床实施,需要进一步的前性,多地点和多模式验证.

关键词:

临床决策支持临床决策支持肝细胞癌是肝细胞癌.大型语言模型患者的表型定型检索增强生成增强生成

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