From code to clinic: challenges in translating ML models into real-world products

Dale Webster

doi:10.1117/12.2660879

7 April 2023 From code to clinic: challenges in translating ML models into real-world products

Dale Webster

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124650U (2023) https://doi.org/10.1117/12.2660879
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Inspired by the potential of artificial intelligence (AI) to improve access to expert-level medical image interpretation, several organizations began developing deep learning-based AI systems around 2015. Today, these AI-based tools are finally being deployed at scale in certain parts of the world, often bringing screening to populations lacking easy access to timely diagnosis. The path to translating AI research into a useful clinical tool has gone through several unforeseen challenges along the way. In this talk, we share some lessons contrasting a priori expectations (“myths”) with synthesized learnings of what truly transpired (“reality”), to help others who wish to develop and deploy medical AI tools

Conference Presentation

Citation Download Citation

Dale Webster "From code to clinic: challenges in translating ML models into real-world products", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650U (7 April 2023); https://doi.org/10.1117/12.2660879

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