The health care industry is arriving at a new era where the medical communities increasingly employ computational medicine and machine learning. Despite significant progress in the modern machine learning literature, adopting the new approaches has been slow in the biomedical and clinical research communities 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.
The Center for Computational Imaging and Personal Diagnostics’ next Machine Learning Working Group Meeting, titled “Incorporating Medical Insight into Machine Learning Algorithms for Learning, Inference and Model Explanation,” will address this topic.
This talk will highlight examples of incorporating medical insight to improve the statistical power of association between various data modalities, design of a novel self-supervised learning algorithm and development of a context-specific model explainer. This general strategy can be employed to integrate other biomedical data.
The meeting will be held today (June 14) from 11 a.m. to noon via Zoom.