Combining clinical expertise and experience with the vast and ever-increasing knowledge of artificial intelligence (AI) has the potential to transform healthcare by providing earlier diagnoses and predicting outcomes. However, today’s AI has inherent risks of error or overconfidence in a prediction.
Sizhe Wang, a graduate student in the lab of Chenyang Lu, the Fullgraf Professor at WashU McKelvey Engineering, developed a framework that teaches clinical AI when to be confident and when to be cautious by providing more trustworthy estimates of certainty and uncertainty in its predictions. The model, called Clinical Uncertainty Risk Alignment (CURA), will be presented at the Association for Computational Linguistics annual meeting in July.
Read more on the McKelvey Engineering website.
