AI for Digital Health and Wellbeing

AI for Digital Health and Wellbeing
Published 10/2025
Duration: 1h 12m | .MP4 1920×1080 30 fps(r) | AAC, 44100 Hz, 2ch | 1.04 GB

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
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.

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.
– Corporate innovation teams working on digital transformation in healthcare and wellbeing sectors.
More Info