Course goal
The participants:
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understand the basics of AI technology and its significance in medicine today
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grasp the foundational ML concepts that underpin AI applications in healthcare.
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identify practical ML applications and their benefits in various medical fields
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critically analyze the impact, expectations, and cautionary aspects of using ML in healthcare.
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gain perspective on the latest advancements and practical advice from leading professionals in the field.
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develop critical thinking and collaborative skills to address complex issues related to ML in healthcare.
Content
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Intro to the transformative role of AI in healthcare, exploring groundbreaking applications and their impact on patient outcomes.
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Detailed exploration of key ML concepts including classification, regression, and the ML workflow, tailored for healthcare applications.
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Examination of specific ML applications in medicine such as anomaly detection (e.g., cancer, carotid calcification), jaundice diagnosis in newborns, and 4D heart models.
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Discussion of the potentials and limitations of ML in medicine, including challenges in integrating algorithmic thinking into decision-making processes.
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Expert insight on the cutting-edge of digital health, sharing real-world applications and research breakthroughs.