Foundations of AI in Healthcare

Artificial intelligence is transforming healthcare by improving diagnosis, enhancing patient care, and streamlining clinical workflows. If you’re a technologist aiming to apply your skills to healthcare challenges, or a healthcare professional eager to understand and shape the AI tools you’ll work with, this course is for you. In this course, you’ll explore the current landscape of AI in healthcare and understand the opportunities and challenges. You’ll then learn about the fundamentals of healthcare data and what makes it unique. You’ll discover why privacy, security, and ethical considerations are critical, and how regulatory frameworks influence the use of AI in medicine. You’ll learn about the machine learning workflow, including defining clinical problems, preparing data, selecting and training models, evaluating performance, deploying solutions, and monitoring results. Key features of this course are guided Jupyter labs on diabetes classification and bias detection, and a final project on liver disease detection. By the end of the course, you’ll have the foundational skills to apply machine learning responsibly, ethically, and effectively to real-world clinical challenges

• Describe how AI and machine learning transform healthcare delivery and patient outcomes
• Explain ethical frameworks, regulations, and governance relevant to AI in healthcare
• Summarize challenges and solutions regarding bias, privacy, and integration in AI healthcare
• Design and implement machine learning workflows tailored to healthcare data

• Basic familiarity with the healthcare domain and terminology
• Readiness to engage in applied exercises or case-based learning