About
Drawing from firsthand experience in radiology at the University of Texas MD Anderson Cancer Center (UTMDACC) and participation in the intracortical Brain-Computer Interface Clinical Consortium (iBCI-CC), this talk explores practical strategies for successfully implementing clinical AI technologies. With a dual focus on medical imaging and neural interface applications, the session provides an in-depth look at how AI can be integrated into clinical workflows in a safe, efficient, and compliant manner. Through real-world examples, attendees will gain insights into evaluating emerging technologies, navigating regulatory pathways, and overcoming the organizational hurdles of AI adoption. The speaker emphasizes validated, evidence-based frameworks that help ensure AI tools not only meet clinical needs but also align with existing systems and improve patient care. Key learning objectives: Learn how AI is currently applied in radiology and neural interface technologies. Understand challenges in clinical workflow integration of AI. Review practical approaches for assessing AI tools for safety and efficacy. Explore regulatory and institutional pathways for clinical AI adoption.
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