Abstract
Artificial Intelligence has become central to innovation in medical diagnostics, offering unprecedented potential for precision, scalability, and clinical relevance. In this seminar, I will share my research journey at the intersection of deep learning, biomedical imaging, and translational medicine, from foundational modeling to the development of deployable, explainable AI systems.
I will present AutoRadAI, the latest translational output from my lab, a modular artificial intelligence framework developed to enhance preoperative staging in prostate cancer by predicting extracapsular extension (ECE) using T2-weighted MRI with ground truth labels derived from postoperative histopathology. Designed to support intraoperative decision-making, AutoRadAI provides probabilistic assessments that can guide surgeons in determining the appropriate surgical margin and extent of dissection. The system was trained and validated on a cohort of 1,001 patients and outperforms human-level benchmarks.
Beyond current applications, I will outline my vision for collaborative AI-driven healthcare innovation, including efforts in cancer diagnostics, reproductive medicine, digital pathology, and neurologic disease detection. My goal is to contribute to UCF’s AI Initiative through interdisciplinary research that translates into meaningful clinical impact across diagnostic and educational domains.
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