Hannah Cornwell: No relevant financial relationships with any proprietary interests.
Learning Objectives:
Define and categorize the core domains of artificial intelligence relevant to cardiothoracic surgery, including machine learning (ML), computer vision (CV), natural language processing (NLP), and artificial neural networks (ANNs).
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Describe current clinical applications of AI across the CT surgical continuum—from preoperative risk stratification to intraoperative assistance and postoperative complication prediction.
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Analyze key studies that demonstrate the impact of AI tools on surgical outcomes, including randomized trials and retrospective analyses, with attention to both clinical effectiveness and safety.
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Evaluate the limitations and challenges of integrating AI into cardiothoracic surgical practice, focusing on data quality, cost, ethical considerations, and physician acceptance.
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Discuss the future directions of AI in CT surgery, including cognitive augmentation, surgical data science, and reinforcement learning in robotic surgery.
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Explore how AI can improve physician decision-making and workflow efficiency, with examples like goal-directed perfusion, suture automation, and video-guided robotic enhancements.
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