Understand AI in medicine, digital health, and wellbeing: clinical ML, multimodal AI & synthetic data to explainability

What you'll learn
- Understand core AI methods—including machine learning, NLP, and deep learning—as applied to health and wellbeing domains.
- Analyze real-world clinical use cases using techniques like multimodal learning, transfer learning, and synthetic data.
- Evaluate challenges in medical AI such as data sparsity, bias, domain shift, and regulatory constraints.
- Design AI workflows integrating domain knowledge, annotation strategies, and human-in-the-loop learning.
- Apply concepts like causal inference and counterfactual reasoning to health interventions and clinical decisions.
- Explore emerging trends like foundation models, hybrid AI systems, and personalized digital health agents.

Requirements
- No prior experience in healthcare or AI is strictly required.

Description
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.

Who this course is for:
- Healthcare professionals curious about how AI is reshaping diagnostics, treatment, and patient support.
- Data scientists and developers looking to break into the digital health and wellbeing space.
- Students and researchers in medicine, psychology, public health, or computer science exploring interdisciplinary AI applications.
- Healthtech entrepreneurs and policy-makers who want to understand the technical, ethical, and regulatory challenges of AI in healthcare.
- Anyone passionate about using technology to improve human wellbeing.
- Executives in pharma, medtech, or insurance exploring opportunities to integrate AI into products, operations, or services.
- Professionals evaluating AI impact for funding, procurement, or regulation.
- Science communicators, journalists, and educators who cover or teach digital health topics.
- VC firms and angel investors evaluating health AI startups or products.
- Corporate innovation teams working on digital transformation in healthcare and wellbeing sectors.
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