Practical application of machine learning to medical problems, including diagnosis (e.g., classifying diseases from X-rays and MRIs), prognosis (predicting survival rates), and treatment (estimating effects from clinical trials).
A deep dive into how AI can transform healthcare delivery, covering predictive analytics, clinical applications of machine learning, and real-world case studies from Stanford Medicine.
trategic frameworks for AI implementation, Responsible AI for healthcare, innovation, and integrating AI into clinical practice.
Blends information technology, data science, and healthcare principles. Topics often include data management, electronic health records (EHRs), privacy, ethics, and applying AI/ML in health settings.