Implementation of AI in Precision Medicine

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. Traditional healthcare models have difficulty addressing the complexity of modern healthcare needs, particularly given the increasingly multimodal nature of health data spanning genetic, clinical, behavioral, environmental, and lifestyle information (Topol, 2023; Judge et al., 2024; Schouten et al., 2025). As precision medicine emerges as a promising solution for integrating multimodal data into healthcare, a new implementation strategy is necessary due to the complexity of existing healthcare structures and the extent of interdisciplinary collaboration that is now required (Tobias et al., 2023).