在精密瘤学中实施生成性人工智能:安全,治理和意义
Ryuji Hamamoto1,2, Takafumi Koyama3, Satoshi Takahashi4,5
1Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. rhamamot@ncc.go.jp.
Journal of hematology & oncology
|February 10, 2026
概括
生成型人工智能通过解释突变,将患者与临床试验相匹配,并支持瘤板来提高精确瘤学. 这项技术旨在提高癌症护理的标准化,成本效益和以患者为中心的决策.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 生物信息学是一种生物信息学.
背景情况:
- 精密瘤学面临着改善患者结果的挑战,需要先进的数据整合工具.
- 生成性人工智能已经迅速发展,为医疗保健的效率和采用提供了潜在的解决方案.
研究的目的:
- 审查生成性AI在精密瘤学的开发和医疗保健应用.
- 概述实施生成人工智能的战略,以加强癌症护理,重点关注突变解释,试验匹配和瘤表型分析.
主要方法:
- 调查生成性AI的历史和当前的医疗保健用途.
- 提出一种使用检索增强生成 (RAG) 和人入循环 (HITL) 工作流程的策略.
- 包括数据标准 (OMOP,mCODE,FHIR),前性评估和合成数据生成.
主要成果:
- 生成性AI可以帮助解释基因突变及其病理意义.
- 人工智能驱动的验证可以简化临床试验的资格匹配.
- 多模式基础模型可以从现实世界的数据计算瘤表型,用于报告起草和分子替代品估计.
结论:
- 生成型人工智能通过提高标准化,成本效益和数据协调,为推进精密瘤学提供了巨大的潜力.
- 拟议的RAG和HITL战略,以及强大的数据治理和合成数据,可以加速以患者为中心的决策和临床试验开发.
- 这种方法为一个新的世界铺平了道路.
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