Artificial Intelligence is transforming healthcare. But the field can feel overwhelming—even for experts.

This course breaks it down clearly and practically.

“AI for Digital Health and Wellbeing” is your structured, up-to-date introduction to the use of AI in healthcare, medicine, and wellbeing. You’ll explore key methods like transfer learning, multimodal AI, few-shot and zero-shot learning, active learning, and synthetic data generation—all explained through real clinical and healthtech examples.

Whether you're a medical professional curious about how AI impacts diagnosis or treatment, a data scientist stepping into the biomedical domain, a healthtech innovator or startup founder, or even a policymaker or investor evaluating AI-driven healthcare solutions—this course is for you.

We connect theory to practice: from understanding how transformer models like BioBERT and Med-PaLM work, to how active learning workflows can reduce labeling burden in clinical NLP. You'll also learn about the challenges of applying machine learning in real clinical settings—data silos, bias, generalizability—and how researchers are solving them.

Finally, we examine the human side of health AI: where explainability matters, how hybrid AI models are making decisions more transparent, and what it takes to build trustworthy, ethical systems for real-world use.

No heavy math or code required—just structured, strategic insight for making sense of AI in digital health today.