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Oral Presentation: TrialChat: Co-Designing Agentic AI for Alzheimer’s Clinical Trial Engagement

Presented at the Healthcare Information and Management Systems Society 2026 Annual Meeting Abstract: Older adults remain underrepresented in Alzheimer's disease and related dementias (ADRD) clinical trials despite bearing much of the disease burden. TrialChat is an AI-powered digital navigator addressing this gap through a conversational chatbot powered by an agentic AI framework and machine learning-based trial matching algorithm. Developed by S-3 Research LLC and CSU Fullerton, and supported by the U.S. National Institute on Aging under the NIH AITC program, TrialChat delivers personalized education and clinical trial recommendations via web and mobile platforms. What distinguishes TrialChat is its co-design approach: older adults and caregivers directly shape its language, flow, and features, elevating voices often overlooked in digital health while designed for commercialization and real-world application. This session shares TrialChat's architecture, early insights, and lessons for scaling inclusive, AI-enabled clinical trial engagement tools across health systems.  Source: https://app.himssconference.com/event/himss-2026/planning/UGxhbm5pbmdfNDM2NTAzMQ==

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Oral Presentation: Reinforcement Learning for Digital Health Interventions in the Dyadic Setting

Abstract: We present our ongoing work on the development of an online reinforcement learning (RL) algorithm for dyadic digital intervention settings in which the task for the RL algorithm is to assist the target person with a difficult illness be adherent to behavioral activities. To achieve this goal the RL algorithm will not only deliver digital interventions to the target person but also deliver interventions to assist the care partner to manage caregiving burden and help the two individuals improve their relationship. That is, different RL components target different elements of the dyad. The RL algorithm is a multi-agent RL algorithm in which the 3 agents make decisions on the 3 elements of the dyad. We incorporate domain knowledge in the form of approximal causal directed acyclic graphs to speed up online learning in this sparse data setting. This work is…

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Oral Presentation: Old School Meets New School: Voice-Based, AI-Enabled Cognitive Rehabilitation for Dementia Care

This is part of the monthly MassAITC webinar series. Abstract: Cognitive rehabilitation therapy supports individuals living with mild cognitive impairment and early-stage dementia in maintaining and improving function in daily life. However, access remains limited due to constraints in the availability and scalability of trained therapists. Recent advances in artificial intelligence, combined with evolving reimbursement pathways for remote care in the United States, now make virtual delivery models increasingly viable, creating new opportunities to expand access to high-quality cognitive care. Moneta Health has developed a telephone-based cognitive rehabilitation platform that enables structured, personalized therapy sessions delivered remotely and overseen by licensed speech-language pathologists. The platform leverages AI-driven speech analysis and automated session orchestration to support consistent therapy delivery while preserving clinician oversight, enabling older adults to engage in care from their homes through a familiar…

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Oral Presentation: Just In Time Adaptive Interventions: Opportunities, Misconceptions, and the Promise of AI-based Algorithms

Murphy, S.A., & Nahum-Shani, I. (2026, January) Just In Time Adaptive Interventions: Opportunities, Misconceptions, and the Promise of AI-based Algorithms. NIH Digital Health Scientific Interest Group (SIG)’s Speaker Series. NIH. Bethesda, MD.    You can watch this presentation here: https://videocast.nih.gov/watch/0b1c35f9-0852-11f1-9f14-124f0a52e769 

Continue ReadingOral Presentation: Just In Time Adaptive Interventions: Opportunities, Misconceptions, and the Promise of AI-based Algorithms