Evaluating AI Applications in Healthcare

As artificial intelligence becomes a core component of modern medicine, the ability to critically assess these tools is essential for healthcare professionals, administrators, and technologists. This course provides a structured approach to understanding how AI functions within a medical context and how to determine if a solution is ready for clinical use. You will move from foundational definitions to sophisticated evaluation frameworks that prioritize patient safety and operational efficiency. What you'll learn: - Understand core AI terminology and its specific applications in modern medical settings - Evaluate AI models for clinical accuracy, reliability, and the mitigation of algorithmic bias - Apply frameworks to measure the downstream effects of AI on patient outcomes and staff workflows - Assess modern generative AI and Large Language Model implementations for healthcare-specific risks - Analyze regulatory requirements and ethical considerations for deploying automated systems - Practice identifying key performance indicators for long-term AI monitoring The course begins with fundamental concepts of machine learning in medicine before exploring practical methodologies for auditing and monitoring AI performance in real-world scenarios. It is designed for beginners in healthcare management, clinical roles, or health technology who need a structured way to judge the quality and safety of AI tools. Start building the skills necessary to lead responsible AI integration in the healthcare sector.