41 Dynamic AI in the Clinical Open World Computer Vision News Yet actually building, approving and deploying medical lifelong learning solutions faces several practical challenges. Our aim with this tutorial is to give participants hands-on insights into how various domain shifts affect the performance of deep learning models in dynamic environments and help them develop strategies to address these issues and correctly monitor performance. We hereby seek to breach the gap in the MICCAI community between technical research on continual learning and the reality of deploying lifelong learning software in clinics. Join our tutorial to learn the technical, clinical and regulatory aspects of developing continual learning solutions. Let us take you through the process of building and deploying medical AI products that learn continuously over their lifetime in our interactive half-day event! We will address the following topics: ❖ Data drift in medical imaging: Common sources of domain shift and their effect on model performance, with a keynote from the fantastic Prof. Jayashree Kalpathy-Cramer. ❖ Continual learning strategies and evaluation: State-of-the-art methods and how to select the appropriate strategy considering performance, flexibility and resource use, with a keynote from Dr. Martin Mundt, a ContinualAI board member. ❖ Current regulations for updating models in different global regions. The event is aimed at a broad audience within our community. Registration is not required, but it does help us assess the number of participants, so please let us know you’rejoining here. Follow us on X at @ContinualMedAI for more updates
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