Towards Explainable and Privacy-Preserving LLM Frameworks for Personalized Healthcare

This project will design, prototype, and evaluate an explainable, human-in-the-loop Large Language Model (LLM) system to enhance patient engagement and personalization in healthcare while ensuring transparency, privacy, and equity. Building on recent findings that current LLM-based healthcare tools lack robust real-world evaluation and interpretability, the research will develop explainability modules, privacy-preserving techniques, and rigorous evaluation frameworks using user studies and simulated environments. The student will gain hands-on experience with cutting-edge LLM architectures, ethical AI design, and applied healthcare research, working in an international, interdisciplinary environment at Toronto Metropolitan University. They will develop highly sought-after technical and research skills, contribute to impactful publications, and build a strong professional network, preparing them for leadership roles in responsible AI development.

Faculty Supervisor:

Glaucia Melo dos Santos

Student:

Partner:

Universidade Federal do Rio de Janeiro

Discipline:

Computer science

Sector:

Education

University:

Toronto Metropolitan University

Program: