Deemea

🚀 Seamless PACS Connection: Faster, Smarter Imaging Data Collection for clinical research 🩻 Accessing and collecting imaging data is now effortless! With Deemea Clinical Research Platform, we ensure a seamless connection to PACS (*), allowing researchers to collect imaging data in just a few clicks. ✅ Direct PACS integration & automated data ingestion: Directly retrieve images from PACS in real-time ✅ Batch & individual transfers: Optimize your workflow with flexible data collection options ✅ Standardized & Secure: Ensure compliance with GDPR & clinical trial regulations ✅ Automated quality control: Detect inconsistencies and missing data instantly. 🔎 You want to focus on your medical discoveries instead of data management? Simplify imaging data collection, maximize research efficiency With Deemea, clinical research teams achieve: ⏱️ 98% reduction in data collection time, accelerating imaging workflows ⚡ ⏩ Study timelines shortened by up to 80% through automation & real-time validation 🚀 40% increase in productivity, enabling the management of more simultaneous studies Deemea transforms imaging data management, allowing researchers to focus on medical discoveries instead of manual processes. 🩻 Ready to revolutionize your clinical research workflow? Contact us 👉 [email protected] #Deemea #clinicaltrials #clinicalresearch #RWE #medicalimaging #imagingdata #RWiD (*) Picture Archiving and Communication System

[LITERATURE REVIEW] Lung cancer is the leading cause of cancer-related deaths worldwide, with 1.8 million deaths in 2020 (IARC). 📖 Deemea highlights an article by Chuang Niu et al. in Nature Communications on how AI enhances lung cancer screening (LCS) LCS with low-dose CT plays a crucial role in early detection, yet its effectiveness remains limited by interpretative variability and incomplete risk assessment. To address this, researchers have developed the Medical Multimodal-Multitask Foundation Model (M3FM), a deep learning framework integrating 49 clinical data types across 17 predictive tasks to improve risk assessment and diagnostic accuracy 📊 ✅ Trained on 163,725 chest CT series, an extensive dataset ranging from lung nodule classification to cardiovascular disease risk assessment ✅ The model’s scalable question-answering architecture allows it to handle high-dimensional images, structured clinical data, and out-of-distribution tasks with minimal retraining ✅ By integrating these data streams, M3FM significantly improves lung cancer risk prediction by up to 20% and cardiovascular disease mortality prediction by 10%, surpassing state-of-the-art models 💡 By leveraging high-dimensional multimodal data, M3FM enables precise and scalable risk prediction, paving the way for more efficient, personalized, and data-driven LCS strategies 🙏🏼 Dalale Gueddouri #clinicaltrials #clinicalresearch #LungCancer #RWE #medicalimaging #imagingdata #RWiD #Deemea

[MEDIA - in French] ✨ Deemea featured in l'Opinion! ✨ 📰 The French HealthTech sector is booming, and AI is at the heart of its transformation. In a recent article by Jon Elizalde in l'Opinion, our CEO Charlotte Pouchy shares how Deemea is aimed to revolutionize clinical trials. 🩻  Traditionally, processing medical images for studies takes up to 24 months. With our AI-powered platform, it now takes just 23 milliseconds, a game-changer for research efficiency. 💡 Data security is also a top priority: our technology ensures sensitive health data never leaves hospitals while enabling collaborative insights. [In French] 🗞️ Read more in l'Opinion: link in comment On this Valentine’s Day, we celebrate our passion for innovation and healthcare 💖 Happy Valentine’s Day to all! A day to celebrate what you love, the passions that drive you, and the innovations that inspire you 💖 #Deemea #clinicaltrials #clinicalresearch #RWE #medicalimaging #imagingdata #RWiD

[MEDIA - in French] ✨ Deemea featured in l'Opinion! ✨ 📰 The French HealthTech sector is booming, and AI is at the heart of its transformation. In a recent article by Jon Elizalde in l'Opinion, our CEO Charlotte Pouchy shares how Deemea is aimed to revolutionize clinical trials. 🩻  Traditionally, processing medical images for studies takes up to 24 months. With our AI-powered platform, it now takes just 23 milliseconds, a game-changer for research efficiency. 💡 Data security is also a top priority: our technology ensures sensitive health data never leaves hospitals while enabling collaborative insights. [In French] 🗞️ Read more in l'Opinion: link in comment On this Valentine’s Day, we celebrate our passion for innovation and healthcare 💖 Happy Valentine’s Day to all! A day to celebrate what you love, the passions that drive you, and the innovations that inspire you 💖 #Deemea #clinicaltrials #clinicalresearch #RWE #medicalimaging #imagingdata #RWiD

💬 [YOUR OPINION] Medical imaging is essential in healthcare, providing key insights for clinical research, diagnosis and treatment. However, the time spent on organizing and managing imaging data during clinical study inclusion can slow down workflows and affect overall efficiency. 📊 Our recent discussions and previous survey results indicate that the time between acquiring a medical imaging exam and seeing its interpretation recorded in the clinical study's eCRF often exceeds 12 months. This highlights a major challenge in clinical workflows. Now, we'd love to hear your perspective on how much time is typically swallowed up by hunting down and organizing imaging data during data entry for clinical study inclusion. ⏱️ 🗳️ Share your thoughts by voting below and let’s discuss! 👇

🚀 AI Action Summit 2025: Key Takeaways and AI & Healthcare in focus From French President Emmanuel Macron’s €109B investment to key discussions on AI’s role in healthcare, research and industry, the AI Action Summit aims to reinforce France’s leadership in the global AI race and accelerate AI adoption across sectors. 🔹 At STATION F, for the Business Day - AI Action Summit, 3 000+ industry leaders, startups, and policymakers explored AI’s strategic impact, regulation, and infrastructure. 🔹 At PariSanté Campus, Deemea welcomed healthcare innovators, researchers, and decision-makers to showcase how our solutions optimize medical imaging data workflows for clinical research and real-world evidence (RWE) studies. 🔹 AI in healthcare is no longer a distant vision. It’s happening now. Scalability, responsible AI, and Europe's leadership in open-source innovation were at the core of every discussion. 🔶 Orange x Mistral AI announced a strategic partnership to accelerate AI development in Europe, reinforcing the continent’s AI sovereignty. 🔶 France now boasts over 4 000 AI researchers across 9 centers of excellence and leading laboratories, driving world-class innovation. 🔶 INESIA, France’s new Institute for AI Safety, was launched to ensure secure and responsible AI deployment in healthcare and beyond. 🔶 The JUNIOR project was unveiled, leveraging AI to model and optimize healthcare systems at a national level. 🔶 IMPACT IA was introduced to assess AI’s real-world effects on healthcare organizations and accelerate clinical adoption. 🔶 France’s AI strategy enters Phase 3, scaling AI infrastructure, training, and adoption across businesses, public services, and society. This includes: - Expansion of AI education, with over 100 000 students trained per year - Deployment of AI in companies and public services to drive innovation - Launch of AI awareness programs like “Cafés IA” to foster democratic debate on AI’s impact. 👏 Huge thanks and congratulations to France Digitale, Future4Care, PariSanté Campus, Bpifrance, La French Tech, and all partners for making this summit a success! ⚡ Now, let’s turn this momentum into action! #Deemea #AIActionSummit #clinicaltrials #clinicalresearch #RWE #medicalimaging #imagingdata #RWiD

🔎 [FEDERATED LEARNING] This week, as France hosts the AI Action Summit, let's explore one of the key approaches enabling privacy-preserving AI: Federated Learning 🔎 🤔 What is Federated Learning? Federated learning is a privacy-preserving AI technique that allows multi-centric model training without transferring raw data from local devices or servers. Instead of centralizing data, the model goes back and forth between different servers, learning locally across multiple devices. This method ensures personal and sensitive data remains private and secure while still enabling organizations to build robust AI models. It is widely used in privacy-sensitive fields like healthcare and mobile technology. 📚 How does federated learning work? Federated learning operates through a decentralized process. The model is sent to each device or server (client) holding local data. The device trains the model locally, and only model updates, patterns or parameters, are sent back to a central server. The server aggregates these updates to refine the global model, improving its performance without ever accessing raw data from individual devices. ✨ Why does federated learning matter? Federated learning addresses the privacy concerns of centralized AI training, which can expose data to breaches or unauthorized access. By keeping data local, it minimizes risks, making it particularly crucial in sectors with strict data regulations, such as healthcare, finance, and telecommunications. It enables AI adoption in industries previously constrained by privacy concerns, allowing for responsible deployment of AI. 🩻 Application in Radiological Imaging Federated learning can revolutionize AI in radiology. Sharing sensitive imaging data like MRI or CT scans across institutions poses privacy risks, but federated learning allows hospitals to collaborate on model training without sharing raw data. For example, hospitals can develop a model for early disease detection using local scans, sharing only the learned parameters. This ensures higher model accuracy while maintaining patient privacy. Implementing federated learning in radiology faces challenges such as ensuring data quality consistency across institutions, maintaining cybersecurity, and managing the computational demands of handling three dimensional images. Powerful infrastructure is needed to efficiently process large datasets such as MRI and CT scans. Federated learning presents a promising solution for privacy-conscious AI applications. Its ability to protect sensitive data while leveraging distributed information makes it highly relevant in today’s data-sensitive world, particularly in fields like healthcare and radiology. ✨ 🙏 Théo Estienne 🔜 Coming soon: LLM & Radiological Imaging #Deemea #clinicaltrials #clinicalresearch #RWE #medicalimaging #imagingdata #RWiD

🇫🇷 🇬🇧 🇪🇺 AI Action Summit: Meet us at PariSanté Campus! 📍Tomorrow, let’s meet at the AI Action Summit! Join us at PariSanté Campus in the afternoon! 🙏🏼 Thank you to PariSanté Campus, Antoine Tesniere and Delphine Muron! #Deemea #PariSantéCampus #AI #Healthcare #Innovation #ClinicalTrials #ClinicalResearch #MedicalImaging #RWE #RWiD

🇫🇷 🇬🇧 🇪🇺 Business Day of the AI Action Summit: Meet us at Future4care’s stand! 📍Tomorrow, let’s meet at the AI Action Summit’s Business Day! Join us at STATION F at Future4Care’s stand (C1) around 10 AM 🫱🏼🫲🏽 🙏🏼 Thank you to Future4Care and Agnès De Leersnyder! #Deemea #Future4Care #AI #Healthcare #Innovation #ClinicalTrials #ClinicalResearch #MedicalImaging #RWE #RWiD

[EVENT: AI Action Summit] - AI & Healthcare 🚀 The countdown is on! Tomorrow marks the "Business Day" at STATION F and "Challenges of AI in Healthcare" at PariSanté Campus, two key events of the AI Action Summit! 📍 Deemea will be at STATION F all day, with a presence at Future4Care’s stand (C1) around 10 AM! 📍 In the afternoon, we will also be at PariSanté Campus where we will be hosting a stand! 🫱🏼🫲🏽 Let’s connect! 🙏🏼 A big thank you to France Digitale, Future4Care and PariSanté Campus, for bringing together such an inspiring ecosystem of startups shaping the future of digital health innovation! #Deemea #Future4Care #FranceDigitale #PariSantéCampus #clinicaltrials #clinicalresearch #RWE #medicalimaging #imagingdata #RWiD