REGULATING AI IN HEALTH RESEARCH AND INNOVATION
summary
Panel explains how EU law splits responsibilities and creates concrete friction: embedded IoHT AI triggers both MDR and AIA rules, forcing duplicated conformity assessments and extra technical documentation that the panel estimates raises compliance costs (impact assessment cites ~€15,000 per AI project). Clinical research relies on pseudonymized clinical trial databases with long retention and validated systems; anonymisation is brittle so the ProCancer‑Eye project uses staged de‑identification plus robustness testing. For consumer mental‑health apps the group urges a data protection certificate scheme and flags EHDS permit delays and data‑quality gaps.
AI-written summary · about
chapters (21)
- 0:00Introduction
- 2:47Internet of Health Things
- 3:30Why AI
- 4:32Embedded AI
- 5:36Safety Performance Quality Requirements
- 8:16Legal uncertainties
- 10:37Conclusion
- 11:50Clinical Trial Context
- 15:52Quality Framework
- 18:59Legal Basis
- 22:14Elizabeth Steinberg
- 22:38How to govern digital practices in mental health
- 23:57Legal framework
- 27:59Consumer perspective
- 33:44Max Koninsender
- 34:08Anonymization
- 35:08What is Anonymization
- 45:22Research
- 47:04Use Case
- 53:27Data and Data Governments
- 58:11Questions
related talks
30mAI Regulation - How to implement AI legally compliant?
01h 10mNavigating the Interface Between the AI Act and GDPR Combining Innovation and ..
01h 08mAI Act in Action Compliance, Oversight, and Enforcement
01h 15mAssessing Fundamental Rights Risks of AI Opportunities and Challenges Ahead
01h 19mAligning Data Privacy and AI Governance
01h 14mNavigating regulatory complexities Enforcing data protection in the field of AI
01h 18mWhich Impact Assessment for AI, Beyond Data Protection
53m10 Key Challenges for AI within the EU Data Protection Framework