Exploring LLMs as a Foundation for Next-Generation Clinical Decision Support Systems

The proposed project aims to develop an AI-powered Patient Assessment and Diagnostic (PAD) tool aimed at aiding in the diagnosis and management of chronic women’s health conditions. Leveraging Large Multi-Modal Models (LMMs), the project seeks to streamline the diagnostic process for hormonal health conditions, addressing the significant gap in timely healthcare access faced by millions globally. There are three main objectives including the design and development of an AI model with and without fine-tuning on a developed and curated women’s health dataset, validation using newly collected anonymized patient data, and optimization of output reports for clinicians, adhering to compliance standards.

Faculty Supervisor:

Marta Kersten Oertel

Student:

Partner:

Healthyher.Life

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

Concordia University

Program: