Overview
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.
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.
No prior experience in healthcare or AI is strictly required.
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.
Prof. Dr. Ana-Maria Olteteanu
Dr. Dr. Ana-Maria Olteteanu is an AI expert, scientist and professor. She specializes in cognitive systems, human-centered AI and generative AI models for creativity and problem-solving.
As an AI Expert for the European Commission and other bodies, Ana-Maria strives to make AI human-centric and focused on human wellbeing.
Prof. Ana-Maria Olteteanu has published 70+ papers on AI models and cognitive systems, is a journal editor and program committee member of various highest rank conferences and workshops on AI, cognitive science and creative problem solving. Ana-Maria loves helping companies and start-ups innnovate and generate value through tech and AI USPs.
As a professor, Ana-Maria has designed and lead university courses for graduate and postgraduate level (Bachelor and Masters), and workshops and courses for business executives and AI professionals. She has build and run courses for Tomorrow University (where she is currently a professor), Freie Universitat Berlin (one of the top 100 universities), Bremen University, Chartered Institute - World's largest Professional Skills Certification Body.
Courses taught:
Algorithms and Data Structures, Generative AI for Business professionals, Applied Data Science, Build with Generative AI, Cognitive Modeling, Computational Creativity, Data analysis for Problem Solving (Tableau), Data Products and Data Solutions, Data Visualization and Storytelling, Ethics and Economics of AI, Human-Centered AI, Introduction to Coding (Java, Python), Introduction to Cognitive Systems and Neuroscience, Machine Learning and Deep Learning, Statistics for Business and Hypothesis Testing among others.