Semester of Graduation
Spring 2026
Degree Type
Thesis
Degree Name
Masters In Information Technology
Department
COLLEGE OF COMPUTING AND SOFTWARE ENGINEERING-INFORMATION TECHNOLOGY
Committee Chair/First Advisor
SHIRLEY TIAN
Second Advisor
LINH LE
Third Advisor
ZHIGANG LI,YING XIE
Abstract
Generative AI, exemplified by large language models like the OpenAI GPT and Meta LLaMA families, can produce diverse content in response to prompts. This capability offers a promising solution to challenges in precision medicine, which seeks to tailor treatments to individual clinical profiles but often struggles with data collection, cost, and privacy concerns. By generating realistic, privacy-preserving patient data, generative AI has the potential to transform patient-centric healthcare. With such motivation, this research develops a comprehensive Generative AI pipeline emphasizing data granularity for accurate prediction of personalized treatments. The pipeline features a central Large Language Model interacting with a Machine Learning agent to determine key factors affecting a patient’s condition. This information is compiled into a query to retrieve personalized suggestions from a guideline database. We exam- in model development, experimental processes, and concerns such as data quality, response evaluation, trust, and reliability. Then, we apply our developed framework on three chronic diseases, namely diabetes, heart disease, and mental illness, on the capability of generating tailored treatment recommendations. Experiments show the proposed framework is a promising step toward explainable, personalized, and clinically aligned AI-driven treatment planning, laying the foundation for future trustworthy, patient-focused medical AI systems.
Comments
A grant of USD 1040 was given by the department to present this paper in IEEE BIG DATA conference held in on December 2025