Dr. Al’Aref took an unconventional route to his current faculty position. After completing his residency and fellowship, he left to work at an imaging institute at Cornell in New York instead of taking a procedural position as an attending faculty member.
In his time at the imaging institute, he worked on CT imaging, non-invasive imaging, and artificial intelligence, using databases in collaboration with other centers to develop more accurate analytic methods for predicting future events using AI.
His professional experience with artificial intelligence has defined much of the research he’s pursued since.
In May of 2020, Dr. Al’Aref accepted a position at UAMS as an Assistant Professor in Cardiology; clinically, he works as an interventionalist, performing procedures in the catheter lab, but he also acts as the Director at Cardiac CT. The combination of both procedural work and imaging is unique, and it informs his approach as a physician and a researcher.
Three different funding opportunities direct much of his time outside of his faculty appointment at UAMS. He is interested in using artificial intelligence to make medicine precise.
With the NIH Research Project Grant, Dr. Al’Aref is developing methods that use artificial intelligence to analyze heart motion in 4-D. The study is focused on cardiac dyssynchrony, which is caused by irregular electronic signals and limits the ability of the heart chambers to function normally.
The standard treatment is cardiac resynchronization therapy (CRT), but before recommending CRT, doctors analyze the heart’s function, which can be difficult. Machine learning could lower the barrier to developing an accurate diagnosis for each patient and improve their outcomes. The use of artificial intelligence and machine learning in this cardiovascular process could speed up analysis and diagnosis in the medical field.
With the NIH Exploratory/Developmental Research Grant, Dr. Al’Aref focuses less on AI and more on biomedical engineering. Working with an engineer at the University of New Mexico, he is developing a pressure sensor that sits in the heart and transmits pressure data via ultrasound. This would be useful for heart failure patients because in addition to the physical exam, the pressure sensor could act as an early warning sign for heart failure patients. Additionally, it can be inserted using a catheter rather than open heart surgery, so it’s a less invasive procedure.
Finally, other research awards have allowed Dr. Al’Aref to explore ways to better detect atrial fibrillation. Using artificial intelligence, he’s examining EKG vectors and hoping to identify certain patterns that point to atrial fibrillation. This research would help to provide better prediction models.
Dr. Al’Aref is passionate about integrating AI into the medical field by partnering with computer scientists and using these tools as an alternative strategy to traditional medical approaches. The potential for machine learning and AI to help with big data and time-consuming analysis could result in better predictive models and more accurate identification of health conditions. He sees AI as a decision support tool that could become another way to inform clinician decisions.