AUTHOR=Yan Adam Paul , Guo Lin Lawrence , Inoue Jiro , Arciniegas Santiago Eduardo , Vettese Emily , Wolochacz Agata , Crellin-Parsons Nicole , Purves Brandon , Wallace Steven , Patel Azaz , Roshdi Medhat , Jessa Karim , Cardiff Bren , Sung Lillian 

TITLE=A roadmap to implementing machine learning in healthcare: from concept to practice

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1462751

DOI=10.3389/fdgth.2025.1462751

ISSN=2673-253X

ABSTRACT=BackgroundThe adoption of machine learning (ML) has been slow within the healthcare setting. We launched Pediatric Real-world Evaluative Data sciences for Clinical Transformation (PREDICT) at a pediatric hospital. Its goal was to develop, deploy, evaluate and maintain clinical ML models to improve pediatric patient outcomes using electronic health records data.ObjectiveTo provide examples from the PREDICT experience illustrating how common challenges with clinical ML deployment were addressed.Materials and methodsWe present common challenges in developing and deploying models in healthcare related to the following: identify clinical scenarios, establish data infrastructure and utilization, create machine learning operations and integrate into clinical workflows.ResultsWe show examples of how these challenges were overcome and provide suggestions for pragmatic solutions while maintaining best practices.DiscussionThese approaches will require refinement over time as the number of deployments and experience increase.