The health care industry is arriving at a new era where medical communities increasingly employ computational medicine and machine learning. Despite significant progress in the modern machine learning literature, biomedical and clinical research communities have been slow in adopting the new approaches due to the lack of explainability and limited data. Such challenges present new opportunities to develop novel methods that address AI’s unique challenges in medicine.
In this talk, Batmanghelich will show examples of incorporating medical insight to improve the statistical power of association between various data modalities, design a novel self-supervised learning algorithm, and develop a context-specific model explainer. This general strategy can be employed to integrate other biomedical data, an exciting future research direction that will be discussed briefly.