Research
My research focuses on developing trustworthy and explainable multimodal artificial intelligence (AI) systems for healthcare. I apply state-of-the-art AI technologies to provide clinical decision support and optimize clinical workflows. Below you will find a sample of the projects I work(ed) on.
WATCH-SS (Warning Assessment and Alerting Tool for Cognitive Health from Spontaneous Speech)
Over 50% of people living with Alzheimer’s disease (AD) are undiagnosed. Many people who have emerging concerns about their cognitive health first consult their primary care physician (PCP), but very few actually receive a diagnosis for cognitive decline due to factors like PCPs’ time constraints, competing priorities, lack of expertise or comfort with AD diagnosis. Thus we are developing WATCH-SS, a screening tool for primary care which passively analyzes a patient’s speech during a clinic visit to assess risk of cognitive impairment (CI). To do this, WATCH-SS runs a set of detectors for five acoustic and linguistic signs of CI and these detections are fed through a predictive model to predict CI. The detectors are a mixture of natural language processing (NLP) -based algorithms and large-language models (LLMs). Our evaluation shows that WATCH-SS achieves strong predictive performance (AUC 0.95 for test and 0.80 for train on DementiaBank data), and its design allows us to effectively explain and verify our risk prediction.