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Explore how synthetic data and deep transfer learning address data scarcity challenges in developing novel medical technologies through this 17-minute conference talk. Learn about the major obstacles faced by developers of medical AI systems when same-domain training data is limited, and discover how SandboxAQ approached these challenges while developing their magnetocardiography (MCG) device for real-time cardiologist decision support. Examine the effectiveness of deep transfer learning with large foundation models and understand the complexities of repurposing models trained on disparate domains for new medical data types. Gain insights into practical strategies for leveraging large-scale synthetic data generation and transfer learning techniques to overcome data limitations in medical device AI development, with real-world examples from cutting-edge magnetocardiography technology implementation.
