Powering medicine with AI
Six students chosen for the inaugural Oberndorf Clinical Artificial Intelligence Scholars Award
Six students were selected to receive the inaugural Oberndorf Clinical Artificial Intelligence Scholars Award. Made possible through a gift from longtime donors Lou and Rosemary Oberndorf and matching funds from the College of Medicine and Intelligent Clinical Care Center, the award supports medical students working with a faculty mentor to create AI-focused team research projects.
Meet the scholars:
Maisha Akbar
Name: Maisha Akbar, class of 2026
Project: AI for Global Health: Developing AI for Cervical Cancer Detection in Haitian Women of African Descent
Mentors: François Modave, PhD, and Albert Kim, PhD
Impact: “Thanks to this award, I will be able to apply AI to bridge global health care disparities.”
Justin Daniels
Name: Justin Daniels, class of 2026
Project: Development of an AI Model for Ultrasound-Based Detection of the Stomach and its Content
Mentor: Meghan Brennan, MD, MS
Impact: “The Oberndorf scholarship is a catalyst to my work in utilizing AI to analyze low-cost medical imaging, ultimately improving patient outreach and outcomes.”
Gabriel Flambert
Name: Gabriel Flambert, class of 2027
Project: Using AI for Equitable Oncology: A Machine Learning Model to Determine Hormone Receptor Status in Breast Cancer Among Women of African Descent in Haiti
Mentors: François Modave, PhD, and Albert Kim, PhD
Impact: “It is my aspiration to continue to try to make the differences I was motivated by when I decided to become a physician.”
Sean Kwak
Name: Sean Kwak, class of 2025
Project: Development of an AI Model to Automate the Segmentation of Key Dermatohistologic Structures
Mentors: Pinaki Sarder, PhD, and Kiran Motaparthi, MD
Impact: “This model will serve as a foundational tool for large-scale extraction and correlation of histologic imaging and clinical datasets to improve dermatologic diagnostics and treatment outcomes.”
Danielle Snyder
Name: Danielle Snyder, class of 2026
Project: Precision in Prediction: Harnessing Machine Learning for an Innovative Endometriosis Prediction Model
Mentor: François Modave, PhD
Impact: “Our initiative has the potential to hasten diagnosis and improve care for thousands of women. This scholarship is an endorsement of innovation as we work toward a collective goal of precision medicine, where innovation meets compassion.”
Daniel Stribling
Name: Daniel Stribling, class of 2025
Project: Individualized Deep Neural Network oncomiR Target Prediction for Personalized Cancer Therapy
Mentor: Kiley Graim, PhD
Impact: “The resources and mentorship provided by this scholarship will make a major impact on my ability to develop more powerful approaches toward AI-driven cancer diagnostics for personalized therapy.”
