Thursday, May 30, 2024

Aaron Fanous among grads of nation’s first dual degree program in medicine and AI

Aaron Fanous among grads of nation’s first dual degree program in medicine and AI

MAY 16, 2024 — When Aaron Fanous graduates this month with both a Doctor of Medicine and a Master of Science in Artificial Intelligence (M.D./M.S. in AI), he will be among the few doctors in the country with a specialization in artificial intelligence.

He and fellow student Eri Osta are making history as the first graduates of the nation’s first known dual degree in medicine and artificial intelligence (AI), a collaboration between the Joe R. and Teresa Lozano Long School of Medicine at The University of Texas Health Science Center at San Antonio and UTSA’s University College. This groundbreaking program, launched in 2023, aims to prepare the next generation of physician leaders in health care through comprehensive training in applied artificial intelligence.

Originally from Tyler, Texas, Fanous’ ambition to become a doctor was inspired by his father's gastroenterology practice, where he witnessed firsthand the profound impact of medical decisions on people's lives. After completing his bachelor’s degree in chemistry at Rice University, Fanous chose The University of Texas Health Science Center at San Antonio (UT Health San Antonio) for its esteemed reputation, collaborative atmosphere and strategic location.

“Going through the M.D./M.S. in AI program was an extraordinary experience.”

During his third year of medical school, Fanous seized the opportunity to enroll in the new dual-degree program, driven by his interest in integrating technology and medicine. Recognizing the slower adoption of AI in health care due to patient privacy concerns, Fanous aimed to pioneer AI applications that enhance patient care and streamline administrative tasks for medical professionals.

“I feel like I was in the right place at the right time in history to have been given the opportunity to enroll in the M.D./M.S. in AI program and get formal education in both fields,” Fanous said. “My mission now is to focus on problems that can be solved with AI and to help other doctors learn how AI and related technologies can benefit their practice, their patients and even their own quality of life.”

For example, AI can optimize many time-consuming administrative tasks, like appointment scheduling and summarizing patients’ charts so that doctors and their staff can spend more time on patient care. Additionally, artificial intelligence can be trained to process medical data to find certain information, for example identifying abnormalities in images generated by X-rays and MRI scans.

The M.S. in AI program is based at UTSA while the M.D. is offered by UT Health San Antonio.

The program’s project-based learning approach proved invaluable for Fanous, allowing him to apply what he was learning in class to solve real-world problems.

“One of the biggest highlights of the program was when I first successfully trained a skin lesion identification model,” he said. “It was exhilarating to have something you make work. Another highlight was taking the work I was doing for school and then building them for presentations and research purposes. This opened my eyes to the amount of work that goes into not only building models, but robustly preparing them for practical use.” 

The curriculum for the MS in AI degree was developed by Dhireesha Kudithipudi, UTSA’s Robert F. McDermott Chair in Engineering, director of the UTSA Neuromorphic Artificial Intelligence Laboratory, and an internationally recognized scholar on the nexus of AI and human well-being.

Referring to Kudithipudi as “a superstar in the AI world,” Fanous credits her for creating the foundation for the robust and meaningful learning experiences in AI that strategically complemented his medical training.

For his capstone project, Fanous worked with UT Health San Antonio radiologist Kal Clark, M.D. to develop an AI model that produces radiology reports from images that can detect and describe what is in the image. In addition, he is exploring methods to optimize the model so it uses less memory while still adhering to security standards so that more clinicians will be able to use this type of technology. Lastly, he is working on a new evaluation metric that more closely aligns AI-generated reports to the radiologist.

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Beyond his academic pursuits, Fanous was part of two teams that competed in the 2023 and 2024 Draper Data Science Business Plan Competitions hosted by the UTSA School of Data Science. The ventures, Aptitude Medical Modeling and SilentSiren, garnered accolades and funding for their innovative health care solutions.

Prior to pursuing a medical residency, Fanous will embark on a one-year postdoctoral position at Stanford University’s Department of Biomedical Science, furthering his training and contributions to medical AI research.

“Going through the M.D./M.S. in AI program was an extraordinary experience,” Fanous said. “Learning to build and develop these AI-powered systems for medical use has helped me understand what is important to both fields and will allow me to bridge the gap between technological innovation and medicine to improve lives.” 

KC Gonzalez

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