Five critical barriers are killing AI’s potential in the European Health Data Space. The main challenge - interoperability. No interoperability, no AI breakthrough. Siloed data means AI models stay blind and ineffective. Even the EHDS, can’t solve them all. Without clear frameworks that balance trust, privacy, and data quality, AI models will never reach their full potential within the EHDS regulation. Here are 5 Critical Barriers to AI Success in the EHDS AI promises to revolutionize healthcare, but five overlooked challenges threaten to stall progress in the EHDS. 1 Data Access & Governance Without clear, trusted frameworks controlling how health data is accessed and shared, AI can’t get the quality data it needs. 2 Interoperability Siloed IT systems block seamless data flow, limiting AI’s ability to analyze diverse and comprehensive datasets. 3 Ethical & Privacy Concerns Patient rights and data privacy must be safeguarded, or AI adoption will face resistance and legal hurdles. 4 Data Quality & Variability Inconsistent and poor-quality data leads to unreliable AI predictions and biased outcomes. 5 Financial Incentives Lack of aligned funding and investment slows AI deployment, especially in resource-constrained public healthcare. What issues are you seeing in benefiting from AI within the EHDS?
Interoperability is the linchpin - without it, even the best AI models remain blind. EHDS will only succeed if trust, data quality, and incentives move in sync
Thank you for this important reminder, Sigrid. It’s hard to imagine this being implemented before 2030 without substantial EU subsidies and fundamental IT infrastructure redoing across member countries. I see vendors in this space winning contracts for patient intake forms supported by Microsoft power pages to keep data in sharepoint. To some this is cutting edge and it will be in 2030. That is the reality. On the upside I’m seeing really well-matured infrastructure readiness to meet EHDS with other vendors where data-pipline engineering will be a simple afterthought compared to mentioned example.
I agree with your points and add one more. The AI platforms today are lacking traceability/ provenance. This is on the outputs of AI; how was that output generated, what was considered, what was the prompt, what was the model revision, and what pathway thru the model determined that output. Without these we should not base life safety actions. We don't accept this level of deficiency in human decisions.
Privacy and ethics are sometimes treated as obstacles rather than foundations. If frameworks balance trust and access correctly, AI could actually scale faster in Europe than anywhere else!!
Spot on. Data quality and governance are just as critical as algorithms themselves.
This is alfa and omega for a system to function within the frame of AI Technology. Sajid Badi-uz-Zaman Saba Q. Zahid Abdullah
Completely agree, interoperability is the foundation for AI in healthcare. Without it, progress stalls. How do you see public-private collaboration addressing these barriers effectively?
The moment theres a population scale longitudinal dataset, health ai will accelerate. We are so far from that right now.
Dmitry Etin what is your take on that?
Director of Quality and Regulatory - HLS | Helping deliver value with trust and efficiency | QMS and Regulatory in Medical Device Software | Lean and Agility believer | Value driven | Specializing in AI Reg. Affairs
2moLook i see it as pressure to EHR, HIE and other vendors to open their siloe to others. If this happens interoperability will not be the problem.