负责任地将人工智能整合到癌症基因组学中：障碍、风险及实现可信赖临床转化的路径
Responsible Integration of AI in Cancer Genomics: Barriers, Risks, and Pathways to Trustworthy Clinical Translation
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- Robert Koch Institute（罗伯特·科赫研究所）
- European Commission（欧洲委员会）
- Deree-The American College of Greece（希腊 Deree 美国学院）
- Åbo Akademi University（奥布学术大学）
- Arcada University of Applied Sciences（阿卡达应用科学大学）
- University of Münster（明斯特大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本综述分析AI与NLP在癌症基因组学临床转化中的障碍，提出应对四大相互关联失败领域的框架与路线图，强调需从系统层面解决问题而非仅提升模型能力。
中文摘要 AI 辅助
人工智能（AI）与自然语言处理（NLP）正越来越多地用于提取、整合和解读与癌症基因组学相关的生物医学知识，但其向常规临床肿瘤学的转化却相对缓慢。核心挑战并非仅在于计算能力，而在于将其可信赖地整合到临床流程中。本综述探讨了NLP与AI如何支撑癌症基因组学流程，涵盖从文献挖掘、自动化变异解读到临床试验匹配、知识图谱构建及多模态数据整合等环节。我们识别出四个相互关联的转化失败领域：证据不一致、可解释性与不确定性、数据治理与可重复性、互操作性。我们未孤立看待这些挑战，而是从系统层面视角聚焦其在转化路径中的相互作用。我们提出了一个概念框架与路线图，通过AI全生命周期内的严格验证、感知不确定性的方法、可互操作的基础设施、监管对齐以及人工监督来应对这些领域。向常规临床应用推进的进展，将更少依赖于进一步提升模型能力，而更多取决于从开发到部署及部署后监测阶段系统性应对这些相互作用的失败领域。
英文摘要
Artificial intelligence (AI) and natural language processing (NLP) are increasingly used to extract, integrate, and interpret biomedical knowledge relevant to cancer genomics, yet their translation into routine clinical oncology has been comparatively slow. The central challenge is not computational capability alone, but trustworthy integration into clinical workflows. This review examines how NLP and AI support the cancer genomics pipeline, from literature mining and automated variant interpretation to clinical trial matching, knowledge graph construction, and multimodal data integration. We identify four interrelated translational failure domains: evidence inconsistency, explainability and uncertainty, data governance and reproducibility, and interoperability. Rather than considering these challenges in isolation, we take a systems-level view, focusing on their interaction across the translational pathway. We propose a conceptual framework and roadmap for addressing these domains through rigorous validation, uncertainty-aware methods, interoperable infrastructures, regulatory alignment, and human oversight across the AI lifecycle. Progress toward routine clinical use will depend less on further improving model capability than on systematically addressing these interacting failure domains from development through deployment and post-deployment monitoring.
