5 papers
Optimizing Sparse Outcomes Through Dense Behavioral Signals via Value-Guided Preference Distillation
Ziyi Zhu, Daniel R. Cahn, Thomas D. Hull +4
Aligning multi-turn dialogue agents is usually framed as matching turn-level human preferences, yet direct optimization of long-term outcomes is often ineffective and prone to rewa…
Healthcare Utilization, Chronic Condition Management, and Workplace Functioning Among Users of a Purpose-Built Mental Health AI (Ash): Cross-Sectional Study
Kristen M. Van Swearingen, Thomas D. Hull, Jeffrey Swigert +1
Mental health challenges can exacerbate physical symptoms and complicate management of chronic conditions. Purpose-built artificial intelligence (AI) tools may offer scalable suppo…
Engagement Phenotypes for a Sample of 102,684 AI Mental Health Chatbot Users and Dose-Response Associations with Clinical Outcomes
Emma C. Wolfe, Ting Su, Olivier Tieleman +3
Background: Conversational AI chatbots are emerging as scalable mental health tools, but little is known about real world engagement or its relationship to clinical outcomes. Objec…
Functional outcomes and naturalistic engagement with a purpose-built conversational AI for mental health (Ash)
Kristen M. Van Swearingen, Thomas D. Hull, Karthik V. Sarma +1
Background: Conversational AI chatbots designed for mental health may offer an accessible, scalable avenue for supporting psychological well-being, yet prior evaluations have large…
Fine-tuning LLMs for Passive Depression Severity Estimation from AI Mental Health Dialogue
Olivier Tieleman, Ziyi Zhu, Ting Su +3
Depression is the leading cause of disability worldwide, and early detection of symptom change is essential for timely intervention. Validated instruments such as the Patient Healt…