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AI for Digital Health and Wellbeing
Published 10/2025
Duration: 1h 12m | .MP4 1920x1080 30 fps(r) | AAC, 44100 Hz, 2ch | 1.04 GB
Genre: eLearning | Language: English
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.
More Info
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RapidGator
NitroFlare
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