梳理AI在精准医疗中落地的障碍与推动力，助力临床应用
Implementation of AI in Precision Medicine
- 基于生态框架分析数据、可靠性、流程、治理四方面挑战
- 识别出影响实际应用的关键制约因素与协同机制
- 适合关注AI临床转化的研究者与医疗管理者
人工智能（AI）在精准医疗中日益关键，能够整合与解析多模态数据，但其在临床环境中的应用仍有限。本文对2019至2024年相关文献进行范围综述，识别出数据质量、临床可靠性、工作流程集成和治理方面的关键障碍与推动因素。通过生态系统框架，揭示了影响现实世界转化的相互依赖关系，并提出未来支持可信且可持续实施的方向。
原文摘要 · Abstract (English)
Artificial intelligence (AI) has become increasingly central to precision medicine by enabling the integration and interpretation of multimodal data, yet implementation in clinical settings remains limited. This paper provides a scoping review of literature from 2019-2024 on the implementation of AI in precision medicine, identifying key barriers and enablers across data quality, clinical reliability, workflow integration, and governance. Through an ecosystem-based framework, we highlight the interdependent relationships shaping real-world translation and propose future directions to support trustworthy and sustainable implementation.
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