L2M – Clinical Trial Recommendation System Using Large Language Model

We are developing a conversational AI system that helps hospitals and cancer centers find the right patients for clinical trials much faster and easier. Currently, research staff spend weeks manually reading through hundreds of pages of patient medical records to see if patients qualify for specific cancer treatment studies, which means many patients who could benefit from new treatments are never found or found too late. Our AI agent can automatically read and understand patient records, medical notes, and lab results, then have natural conversations with healthcare workers to recommend which trials might be good matches for each patient. This system will save significant time and money by reducing the manual work needed to screen patients, help more cancer patients access potentially life-saving treatments, and enable smaller hospitals to participate in clinical research that they couldn’t handle before due to limited staff resources. The conversational interface makes the technology easy for busy healthcare professionals to use without extensive training, ultimately improving patient care while reducing administrative burden on medical staff.

Faculty Supervisor:

Behrouz Far

Student:

Partner:

Edmonton Unlimited

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Public administration

University:

University of Calgary

Program: