Artificial Intelligence (AI) is playing a more and more important role in medicine. One of the main reasons that AI models caught lots of attention is their superior quantitative performance, but there are many other aspects that we need to consider in order to “translate” these models into real clinical workflow, such as transportability, transparency, actionability and privacy.
In this presentation, I will discuss the considerations of these aspects when developing a translational clinical AI model with concrete examples, with the hope of providing a holistic picture of this topic and the path forward.
Fei Wang is an associate professor in Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine (WCM), Cornell University. He is also the founding director of the WCM Institute of AI for Digital Health (AIDH). His major research interest is AI and digital health. He has published more than 300 papers on the top venues of related areas such as ICML, KDD, NIPS, CVPR, AAAI, IJCAI, Nature Medicine, JAMA Internal Medicine, Annals of Internal Medicine, Lancet Digital Health, etc. His papers have received over 27,000 citations so far with an H-index 79. His (or his students’) papers have won 8 best paper (or nomination) awards at top international conferences on data mining and medical informatics. His team won the championship of the AACC PTHrP result prediction challenge in 2022, NIPS/Kaggle Challenge on Classification of Clinically Actionable Genetic Mutations in 2017 and Parkinson’s Progression Markers’ Initiative data challenge organized by Michael J. Fox Foundation in 2016.
Dr. Wang is the recipient of the NSF CAREER Award in 2018, as well as the inaugural research leadership award in IEEE International Conference on Health Informatics (ICHI) 2019. Dr. Wang also received prestigious industry awards such as the Sanofi iDEA Award (2021, 2023), Google Faculty Research Award (2020) and Amazon AWS Machine Learning for Research Award (2017, 2019 and 2022). Dr. Wang’s research has been supported by a diverse set of agencies including NSF, NIH, ONR, PCORI, MJFF, AHA, etc.
Dr. Wang is the past chair of the Knowledge Discovery and Data Mining working group in American Medical Informatics Association (AMIA). He is a fellow of AMIA, a fellow of IAHSI, a fellow of ACMI and a distinguished member of ACM.