Frontiers of AI in Medical Imaging for Clinical Decision Making
Daniel Rubin is Associate Professor of Biomedical Data Science, Radiology, Medicine (Biomedical Informatics Research), and Ophthalmology (courtesy) at Stanford University. His NIH-funded research program focuses on quantitative imaging and integrating imaging data with clinical and molecular data to discover imaging phenotypes that can predict the underlying biology, define disease subtypes, and personalize treatment. He has published over 160 scientific publications in biomedical imaging informatics and radiology. In this talk Daniel looks at these points…
1. There is much variability in people and their diseases, necessitating “precision medicine” and “precision health.”
2. There are enormous amounts of data that can be leveraged to improve clinical decision-making.
3. Integrating various types of data (e.g., images + clinical notes) and handling longitudinal data is needed.
4. AI methods that leverage integrated data can provide clinical decision support in several types of clinical decision scenarios.