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Explore the distinctive challenges and practical considerations of integrating Artificial Intelligence and Machine Learning into medical software development through this 16-minute university lecture. Learn to differentiate AI/ML applications from traditional software development approaches, focusing on the critical importance of training data quality, model development processes, and reproducibility standards. Examine key risk factors that threaten AI/ML system reliability, including distributional shifts, adversarial attacks, and performance drift over time. Understand how to approach AI/ML components as inherently "unsafe" systems requiring robust design strategies and failure mitigation approaches, including human-in-the-loop methodologies. Discover the specialized workflows necessary for training and deploying AI/ML models in clinical environments, emphasizing the need for continuous post-market monitoring and quality control. Master the principles of risk assessment specific to medical AI/ML applications and develop strategies for managing the "black box" nature of these systems in healthcare settings where reliability and transparency are paramount.
