Implementation of AI in Precision Medicine
Göktuğ Bender, Samer Faraj, Anand Bhardwaj
TL;DR
This scoping review maps the 2019–2024 literature on implementing AI in precision medicine, revealing that while AI enables multimodal data integration and adaptive care (e.g., digital twins, multi-omics), real-world adoption is hindered by fragmented data, variable clinical reliability, workflow misalignment, and governance gaps. The authors propose an ecosystem framework that treats governance, data quality, clinical reliability, and workflow integration as interdependent dimensions that collectively shape translation. Key contributions include identifying prevalent targets (oncology, cardiology), outlining concrete data-standardization and validation needs, and calling for Explainable AI and dynamic governance to ensure safety, equity, and trust. The work underscores that scalable, sustainable implementation requires coordinated policy action, interoperability, continuous model monitoring, and clinician–AI collaboration to align computational insights with clinical judgment.
Abstract
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.