Translating Machine Learning and Artificial Intelligence in Medicine: Focus on Clinical Impact

Artificial intelligence is reshaping modern medicine at an unprecedented pace. Predictive models now rival or exceed traditional clinical tools in accuracy, yet many remain confined to research settings, far from the bedside where impact truly matters. As healthcare systems face mounting pressures from chronic disease burden to workforce shortages; the need for clinically deployable, trustworthy, and equitable machine learning solutions has never been more urgent.Despite rapid methodological advances, translating algorithms into meaningful improvements in patient outcomes remains a central challenge. High performance metrics alone are insufficient. Clinical integration, interpretability, real-world validation, fairness, and measurable health impact must define the next phase of innovation.The focus of this Research Topic is to accelerate the transition from algorithm development to tangible clinical benefit. We seek to showcase rigorous, translational work that demonstrates how machine learning can enhance diagnosis, refine risk stratification, optimize treatment decisions, improve medication safety, and strengthen health system performance. Emphasis will be placed on studies that prioritize clinical applicability, implementation feasibility, equity considerations, and demonstrable outcome improvement.This collection welcomes manuscripts which discuss the impact on clinical practice and outcomes in terms of patient care of the following advances:• Clinically actionable prediction models • Explainable and trustworthy AI for medical decision-making• Early detection and prevention strategies • Innovations in medication safety and therapeutic optimization• AI integration into electronic health records and clinical workflows• Multimodal and foundation models with demonstrated clinical utilityBy bringing together clinicians, data scientists, informaticians, health services researchers, and policy leaders, this Research Topic seeks to redefine success in medical AI not by algorithmic novelty alone, but by real-world clinical impact. We invite forward-thinking contributions that will shape the next generation of responsible, scalable, and practice-changing machine learning in medicine. Topic editors: Md.Mohaimenul Islam, Ming-Chin Lin Source: https://www.frontiersin.org/research-topics/79630/translating-machine-learning-and-artificial-intelligence-in-medicine-focus-on-clinical-impact