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Prospective Real-World Implementation Of Deep Learning Systems In Healthcare: A Systematic Review Guided By Implementation Science
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.
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Deep learning systems are proving effective and feasible when integrated into real clinical workflows across diverse medical specialties, but research on long-term sustainability, cost, and broad clinician and patient acceptability remains limited. Practical deployment of these AI tools requires structured implementation strategies informed by implementation science to ensure smooth adoption into everyday healthcare practice.
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