Among AI’s great promises in relation to medicine is its potential to use existing patient data—including MRIs—to identify and diagnose potential problems. Doing so has many potential benefits, including lower costs and fewer invasive patient procedures.
Among the researchers making good on this promising AI potential is Mohamed Shehata.
Shehata is a postdoctoral fellow in the Department of Bioengineering at the University of Louisville. He’s won numerous awards for his work using machine learning to design computer-aided diagnostic and prediction systems for the early detection of medical issues, including tumors and post-transplant kidney rejections.
Shehata is also one of Computing’s Top 30 Early Career Professionals for 2024. In the following Q&A, he describes
His award-winning dissertation on how machine learning models could be applied to patent data and medical imaging for early diagnosis of kidney diseases
The non-invasive, cost-effective AI-based system he developed to detect acute renal transplant rejection at earlier stages than were previously possible
How improving patient outcomes and pushing the boundaries of medical science drive his work and fuel his curiosity
What he’s learned lately, including about the value of failure and of collaborating with experts in other fields
You received the MIT Technology Review Award for Innovators Under 35 in the MENA region in 2021. Can you share the project or innovation that led to this recognition, and what impact it has had on the field of medical imaging?
In 2021, I received this MIT Technology Review Award for developing an AI-based system for the early detection of acute renal transplant rejection using MRIs. This system leverages deep learning algorithms to analyze MRI scans, identifying subtle changes in kidney tissue that may indicate rejection at an early stage.
... continue reading