Implementation of AI in Precision Medicine
It addresses the limited real-world adoption of AI in precision medicine, offering insights for researchers and practitioners, but is incremental as a review.
This paper conducted a scoping review of AI implementation in precision medicine from 2019-2024, identifying key barriers and enablers such as data quality and clinical reliability, and proposed an ecosystem-based framework to guide future translation efforts.
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.