Technical Event
Panel Discussion: Large-Scale Artificial Intelligence Deployment and Evaluation in Clinical Practice
6 August 2025 • 3:15 PM - 4:45 PM PDT | Conv. Ctr. Room 4
Panel Moderators:
Axel Wismüller, Univ. of Rochester Medical Ctr. (United States)
Joana Pereira, Karolinska Institute (Sweden)
The integration of artificial intelligence (AI) in healthcare, particularly using advanced deep learning and generative methods, presents substantial challenges and opportunities. Implementation of AI in clinical settings, such as radiology, requires evaluation beyond algorithm development. Measuring the effectiveness of AI is complex: Should success metrics be based on the number of AI uses in radiology, the variety of pathologies analyzed, throughput, user engagement, or clinical quality measures like diagnostic accuracy or study turnaround time? Economic factors like return on investment and regulatory constraints are also critical. The panelists, drawing on multi-year practical experience in large-scale AI deployment in radiology, will discuss methods to effectively implement, evaluate, and enhance the value of AI in medical imaging. This includes methods, which assess radiologists' performance with and without AI, and can provide advances like revised radiology quality assurance programs, or improved multi-institutional public health tracking. Following a series of introductory multi-speaker presentations, the panel discussion will cover in-house AI system development, the use of AI in clinical research, and FDA regulatory aspects.
This panel is part of the Emerging Topics in Artificial Intelligence (ETAI) 2025 conference.
SETUP: Classroom and theater style seating.
Axel Wismüller, Univ. of Rochester Medical Ctr. (United States)
Joana Pereira, Karolinska Institute (Sweden)
The integration of artificial intelligence (AI) in healthcare, particularly using advanced deep learning and generative methods, presents substantial challenges and opportunities. Implementation of AI in clinical settings, such as radiology, requires evaluation beyond algorithm development. Measuring the effectiveness of AI is complex: Should success metrics be based on the number of AI uses in radiology, the variety of pathologies analyzed, throughput, user engagement, or clinical quality measures like diagnostic accuracy or study turnaround time? Economic factors like return on investment and regulatory constraints are also critical. The panelists, drawing on multi-year practical experience in large-scale AI deployment in radiology, will discuss methods to effectively implement, evaluate, and enhance the value of AI in medical imaging. This includes methods, which assess radiologists' performance with and without AI, and can provide advances like revised radiology quality assurance programs, or improved multi-institutional public health tracking. Following a series of introductory multi-speaker presentations, the panel discussion will cover in-house AI system development, the use of AI in clinical research, and FDA regulatory aspects.
This panel is part of the Emerging Topics in Artificial Intelligence (ETAI) 2025 conference.
Event Details
FORMAT: Panel discussion followed by audience Q&A.SETUP: Classroom and theater style seating.