Christophe MATTLER

📢 Attention Digital Health Experts: A Key Study on AI in Healthcare Dear digital health professionals, if you haven't had the chance to explore it yet, our insightful study on the potential of Artificial Intelligence in healthcare is a must-read. With European healthcare systems facing significant challenges, AI could be a transformative solution. Our study, released in July, highlights: 🔍 Key Barriers to Adoption: Addressing data limitations, and tackling legal, organizational, and cultural challenges. 🏥 Real-World Success Stories: Learn from hospitals successfully integrating AI technologies. 🛠️ A Proposed Framework: Detailed strategies to monitor and accelerate AI integration across EU healthcare systems. This is an invitation to dive into how we can safely, ethically, and effectively lead AI innovation while protecting patients' rights. Don't miss out on driving the future of digital health—check out the study if you haven't yet: 👉 https://lnkd.in/eN8uNEei #HealthUnion #DigitalHealth #EHDS #AI

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AI Healthcare architecture in 07 simple layers Most AI healthcare projects fail before they even start, just because the foundations are missing. Without structured layers, you get: ◾ Biased data ◾ Siloed systems with no interoperability ◾ Models that can’t scale or stay compliant ◾ Clinical teams who never adopt the output That’s why every successful AI healthcare system is built layer by layer: ✅ Data Foundations -Clean, representative, multi-source inputs ✅ Secure Platform -Governed, mapped, and compliant data sharing ✅ Clinical Features -Extracted, clinically valid signals ✅ Model Intelligence -Calibrated, task-fit predictions ✅ Clinical Engines -Decision layers with human oversight ✅ Point-of-Care -Embedded workflows in EHRs and imaging ✅ Trust & Oversight -Continuous monitoring and recalibration If even one layer is skipped, trust collapses. Precision care needs precision architecture. 📌 Save this post to blueprint your next AI initiative. 🔔 Follow Rizwan Tufail for frameworks that make AI in healthcare work safely, ethically, and at scale.

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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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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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I love this initiative: $1M #Alzheimer’s Insights #AI Prize ⏰ Calling on researchers, technologists, and innovators to leverage agentic AI to develop solutions to speed up breakthroughs in #ADRD (Alzheimer’s & related dementias) research. 🔬 #AgenticAI can help speed up and scale Alzheimer’s research by generating new ideas, harmonizing datasets, and improving experimental design. ✅ 55 million people worldwide are living with #ADRD ✅ The need for breakthroughs is urgent, and #AI can help ✅ Contribute to a big leap in the fight against #ADRD and win 1 million USD ✅ Submission deadline: Sept 12, 2025 More info on the Alzheimer's Disease Data Initiative website.

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How to feel confident answering digital health questions from patients? For medical professionals, digital health is no longer “nice to have” because patients are googling symptoms, using apps, wearables, and even AI. But when they ask you, sometimes it’s hard to know what’s reliable. That’s why I wrote Guide to Digital Health — a free handbook collecting what you should know: how to evaluate apps & sensors, health-privacy, genetic tests, even correcting misinformation. If you think this could help someone in your network (doctor, nurse, student, patient advocate), please share this post so it reaches them. And of course, download it for yourself here. https://lnkd.in/dA9YHZSm

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