Dr. Kai Heitmann

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?

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3 Healthcare AI papers I'm reviewing today 1. 📚AI in Medicine: Medical multimodal foundation models in clinical diagnosis and treatment: Applications, challenges, and future directions https://lnkd.in/eNV5CDSp Medical Multimodal Foundation Models (MMFMs) combine diverse data types (imaging, text, labs) to improve diagnosis, treatment planning, and precision medicine. Recent advances in large datasets and multimodal architectures (vision-only and vision-language) enable strong generalization across tasks like segmentation, classification, and clinical report generation. Key opportunities lie in holistic integration of multi-organ/multimodal data, but challenges remain in optimizing representations and scaling real-world clinical adoption. 2. 📚 BMJ Dig Health & AI - Optimising large language models for clinical information extraction: a benchmarking study in the context of ulcerative colitis research https://lnkd.in/erXhhX9k This study compared open-source and closed-source LLMs for extracting the Mayo Endoscopic Subscore from colonoscopy reports. It found that QLoRA fine-tuning improves open-source performance significantly, but GPT-4o with prompt engineering still outperforms them by 5–11% and is more cost-effective. Overall, GPT-4o is the most efficient option today, while QLoRA-optimized open-source models are viable fallbacks, though both leave room for improvement in instruction following. 3. 📚 JAMIA Open - Generative artificial intelligence for automated data extraction from unstructured medical text https://lnkd.in/ekfy8-VX A GenAI pipeline using an open-source LLM was developed to extract structured data from right heart catheterization notes with built-in guardrails and a retry mechanism. It achieved high performance (99% precision, 85% recall, 91.5% F1, 90% accuracy), with missed values as the main error and hallucinations extremely rare (<0.01%). The study shows LLM pipelines can reliably mine unstructured clinical data, improving research efficiency and clinical applications.

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