AI for Innovation in Health: The Blended Learning and Innovative Assessment Module

Orientation to TEF-Health and to the disruptive blended-learning pedagogy used across the curriculum, including TRL/CML maturity concepts and an overview of AI & deep learning in health.

• Introduction
• Pedagogy

How national healthcare systems work across Europe, and how clinical data warehouses and population data feed AI-driven healthcare.

• General
• Clinical Data Warehouse
• Healthcare systems

Regulatory landscape for medical AI — homologation, certification and the EU AI Act, from a Notified Body perspective.

• Notified Body

Biomedical ethics for AI in health: from foundational principles and co-design methods to concrete use cases — bias, trustworthy AI, robotics in care, cyber security.

• Introduction
• Methods
• Use cases

How health data is collected, structured, labelled and contextualised, and how Clinical Data Warehouses fit into the AI software lifecycle (MDR / IVDR).

• Data collection, structuring & labelling
• Clinical Data Warehouse
• Contextualization
• Software AI

From clinical research design and stakeholder duties to validating AI software in the clinic — usability, federated learning, real-world evaluation case studies.

• Clinical Assessment
• Software AI

The full Health-AI entrepreneur journey: value proposition, customers, technology, funding, strategy and scale-up.

• Entrepreneurial spirit

Pitching, storytelling and non-verbal communication for Health-AI founders, plus offer-and-demand perspectives on medical devices and surgical robotics.

• Communication
• Offer and Demand