Prospective real-world implementation of deep learning systems in healthcare: a systematic review guided by implementation science.

ABSTRACT: Deep learning (DL) applications in healthcare are expanding beyond proof-of-concept studies. Yet, the extent of its real-world implementation and impact on patient care and clinical workflows remains unclear due to the limited prospective real-world findings. Understanding how DL tools perform in real clinical environments is critical for guiding successful and sustainable deployment. Using a layered methodology with established implementation science frameworks, this systematic review aimed to systematically map the implementation strategies and outcomes of prospective DL implementation studies, proposing recommendations based on identified gaps of relevant studies to serve as a guide for the future implementation of DL systems. 20 articles were included: 3 from radiology, 1 from otolaryn

SUBMITTER: Tseng RMWW 

PROVIDER: S-EPMC12913662 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature