Pamela Z. Cacchione, University of Pennsylvania. Li Shen, University of Pennsylvania. Heart Failure Monitoring Socks will be used in hospitalized persons with heart failure to gather data on heart failure exacerbations and responses to treatment. We will use this data to develop predictive models for edema and fatigue due to heart failure.
Katie Seaver, The Babel Group. Rachel Khasky-Levy, The Babel Group. This project will evaluate a personalized speech recognition app built by Voiceitt for adults aged 55+ with severe dysarthria, focusing on accuracy, usability, and user satisfaction. Participants will train the app on their own speech and use it in daily life, with support from The Babel Group.
Jesse Poganik, Brigham and Women's Hospital This pilot project leverages artificial intelligence and advanced aging biomarkers to identify existing FDA-approved medications that may slow biological aging, using clinical data and biospecimens from over 155,000 participants in the Mass General Brigham Biobank. By focusing on approved drugs with established safety profiles, this approach enables rapid translation of findings into clinical practice for promoting healthy aging and preventing age-related diseases.
Eun Kyoung Choe, University of Maryland-College Park This pilot develops Aidara, an AI-powered digital health system that helps caregivers of individuals with cognitive impairments make safer over-the-counter medication decisions. Through multimodal interaction and personalized guidance, Aidara aims to support informed decision-making and reduce medication-related risks.
Wenyao Xu, Auspex Medix. Wei Sun, University at Buffalo. This project is to investigate an AI-powered, smartphone-based hearing screening tool that uses non-volitional pupillary responses to objectively assess hearing functions and loss.
Gregory Stock, Socratic Sciences Inc. Hamed Zamani, University of Massachusetts. Mary Mittleman, New York University. Socratic Sciences, in collaboration with UMass and NYU, is developing an AI-facilitated, question-driven app to help family caregivers of those with AD/ADRD connect and support one another in small, trusted groups around open-ended questions. The project will test and refine a group-facilitation AI bot to make meaningful peer support scalable, accessible, and affordable for millions of overwhelmed caregivers.
Justin Chan, Carnegie Mellon University. Swarun Kumar, Carnegie Mellon University. Neelesh Nadkarni, University of Pittsburgh. The proposed work uniquely aims to measure pulse transit time and blood pressure across different arterial points across the body using the reflections of wireless signals from a single AI-enabled mmWave radar device, which is a key enabler towards whole-body blood flow monitoring both in home and clinical environments.