De Cock, MartineFilienko, Daniil2025-05-122025-05-122025Filienko_washington_0250O_27861.pdfhttps://hdl.handle.net/1773/52911Thesis (Master's)--University of Washington, 2025The rapid advancement of Large Language Models (LLMs) has opened new avenues for AI-assisted healthcare, particularly in chronic disease management. This study explores the application of in-context learning methods to enhance LLMs' ability to deliver Problem Solving Therapy (PST) and support tuberculosis (TB) treatment adherence. We investigate how LLMs can improve the quality and empathy of AI-driven therapy sessions. Additionally, we propose the integration of LLMs into digital adherence technologies to facilitate interactive patient-provider communication during TB treatment. We leverage prompt engineering, Retrieval Augmented Generation (RAG), and multi-agent systems. Our evaluation across both projects employs both automatic metrics and expert human assessment to analyze the effectiveness of these AI-driven interventions. Findings indicate that while LLMs provide a promising tool for enabling better ongoing care for people with chronic disease across different fields, challenges remain in maintaining privacy, safety, and ethical considerations. This research contributes to the growing field of AI-enhanced healthcare, highlighting the potential and limitations of LLMs in bridging mental health and infectious disease treatment gaps.application/pdfen-USCC BYHealth AILarge Language ModelsLLMsComputer scienceComputer science and systemsThesis