Doctors Modave and Brennan mentor med students with AI projects

François Modave, Ph.D., professor of anesthesiology and associate chair for research, and Meghan Brennan, M.D., M.S., assistant professor of anesthesiology, are mentoring future doctors with artificial intelligence (AI) projects.

AI Mentoring Brennan Modave

The University of Florida College of Medicine and the Intelligent Clinical Care Center awarded six medical students the Oberndorf Clinical Artificial Intelligence Scholars Award. This one-year research award supports the students to join with a faculty mentor to create AI-focused team research projects.

Congratulations to the inaugural cohort of Clinical Artificial Intelligence Scholars: Maisha Akbar, Justin Daniels, Gabriel Flambert, Sean Kwak, Danielle Snyder, and Daniel Stribling! Akbar, Flambert and Snyder are being mentored by Modave and Daniels is being mentored by Brennen.

Akbar’s project is titled “AI for Global Health: Developing AI for Cervical Cancer Detection in Haitian Women of African Descent.” This project aims to develop and validate a model tailored to the needs of Haitian women, bridging a gap in global health equity, and advancing the field of artificial intelligence in medicine.

Daniels’ project is titled “Development of an AI Model for Ultrasound-Based Detection of the Stomach and its Contents.” This project aids the integration of advanced technology into patient-centered health care, with a particular focus on anesthesia and perioperative medicine.

Flambert’s project is titled “Using AI for Equitable Oncology: A Machine Learning Model to Determine Hormone Receptor Status in Breast Cancer Among Women of African Descent in Haiti.” This project seeks to use an existing dataset of scanned hematoxylin and eosin slides, drawn from women with breast cancer from a pathology lab in Haiti, to build an AI model to predict estrogen receptor positivity. 

Snyder’s project is titled “Precision in Prediction: Harnessing Machine Learning for an Innovative Endometriosis Prediction Model.” This project aims to provide an alternative to expensive and often invasive diagnostic procedures in order to diagnose endometriosis preoperatively.

Congratulations to all!

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