Raj Praveen M’s Post

Modella AI, Inc. has announced the launch of its multimodal foundation models and generative AI copilots to revolutionize pathology and enhance diagnostic precision. Originating from the Mahmood Lab at Harvard Medical School and Mass General Brigham, these AI models improve accuracy, efficiency, and interactivity in medical imaging analysis. By leveraging extensive datasets, the models support tasks like disease detection, prognosis, and therapy prediction, providing scalable solutions to global medical needs. Follow the link to read the article in full... https://lnkd.in/eW4VaHeM #digitalpathology #pathologynews

𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐃𝐨𝐦𝐚𝐢𝐧-𝐄𝐧𝐫𝐢𝐜𝐡𝐞𝐝 𝐀𝐈 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐈𝐦𝐚𝐠𝐢𝐧𝐠 Given a disease & a medical imaging modality, every AI solution should be directed towards the following: - 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐌𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐈𝐦𝐚𝐠𝐢𝐧𝐠 & 𝐃𝐢𝐬𝐞𝐚𝐬𝐞 𝐭𝐨𝐠𝐞𝐭𝐡𝐞𝐫. 1) For radiology, segmentation should be the core of the Foundation Model because it gives better interpretation. For example, segmentation says, "Hey, this is the Left Lung " and "This is the Right Lung." it is infused into the vision model. This should ideally use a Segment Anything model, and fine-tune it on the new disease data, to have better segmentation. 2) Align the Textual "Left Lung" with the Visual "Left Lung" by fine-tuning using an already existing Medical Data based Foundation Model like MedGPT. 3) For aligning, and using it for Disease Classification careful engineering of the vision additions, and tuning it to the corresponding textual aspects is important. MedCLIP and similar such architectures can help in such alignment. This is more important in resource-constrained settings. This is because you must understand how to engineer new models and fine-tune with proper disease etiology understanding, where only data availability is in the order of hundreds, or at max lower thousands. - The Foundation Model for Disease + Imaging Modality based on the General Foundation Model is the future of Medical Imaging AI with Domain-Enrichment. Just fine-tuning is as useless as going to the moon without oxygen. It adds no value, and hence it will perish, soon. Please be aware. AI is more capable than just fine-tuning. You have to solve the specific problem in a better, more domain-enriched way. #statistics #machinelearning #deeplearning #datascience #artificialintelligence #mathematics

𝐀𝐈 𝐢𝐧 𝐏𝐚𝐭𝐡𝐨𝐥𝐨𝐠𝐲: 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐎𝐧𝐞 𝐒𝐥𝐢𝐝𝐞 𝐚𝐭 𝐚 𝐓𝐢𝐦𝐞 𝑫𝒐𝒘𝒏𝒍𝒐𝒂𝒅 𝑷𝑫𝑭 𝑩𝒓𝒐𝒄𝒉𝒖𝒓𝒆 - https://lnkd.in/dkrGmDX7 The AI in pathology market refers to the commercial space where companies develop, market, and provide products and services that incorporate artificial intelligence technologies specifically designed for pathology applications. This market focuses on leveraging AI algorithms, machine learning techniques, and advanced computational tools to enhance and automate various aspects of the pathology workflow. The global AI in pathology market is projected to reach USD 169.8 million by 2029 from USD 82.8 million in 2024, at a CAGR of 15.4 % over the forecast period. The region leading the pathology AI market, North America, is driven by existing infrastructural developments within the healthcare system, favorable regulations as well as the readiness of the market to accept such technologies as advanced diagnostics. The US and Canada are at the forefront of investing in diagnostics particularly digital pathology and artificial intelligence (AI) technologies aimed at improving diagnosis accuracy and efficiency, especially towards challenging diseases like cancer. While these and other initiatives such as the US FDA's fast-tracked approval of advanced medical equipment like ‘AI by the NCI, have spurred the pathology market to embrace AI technologies quicker than any other market. #AIinPathology #PathologyInnovation #ArtificialIntelligence #DigitalPathology #HealthcareAI #MedicalAI #PathologyTrends #AIHealthcareSolutions

Brain Tumor Segmentation with advanced Vision Transformer (ViT) technology! This project leverages state-of-the-art deep learning and transformer architectures to enhance medical imaging precision. From custom patch embedding to multi-head attention mechanisms and optimized loss functions like Dice Loss, every component is fine-tuned for exceptional performance. Achieving remarkable accuracy and Dice Coefficient metrics, this innovation represents a significant leap in healthcare AI. Explore the future of medical image analysis, powered by transformers and AI-driven segmentation. #BrainTumorSegmentation #VisionTransformers #DeepLearningInHealthcare #AIForMedicalImaging #HealthTech #AIInnovation

Dear Connections, 🩺 Breakthrough in Medical AI: Kidney Tumor Detection 🚀 Excited to share our latest AI breakthrough in medical imaging! We've developed a cutting-edge machine learning model that can accurately classify kidney tumors from CT scan images with an impressive 98% accuracy. 📊🧬 Key Highlights: Advanced deep learning algorithm for medical image analysis Simple upload process for CT scan images Instant classification: Normal vs. Tumor 98% prediction accuracy revolutionizing early detection How it works: Upload your CT scan image Our AI instantly analyzes the scan Receive immediate classification results This technology has the potential to: Accelerate diagnostic processes Reduce human error Enable earlier interventions Support healthcare professionals Github link: https://lnkd.in/gPygc9ii #MedicalAI #HealthTech #ArtificialIntelligence #MachinelearningInHealthcare #Innovation

Recent advancements in artificial intelligence (AI) are revolutionizing the field of medical imaging, specifically in enhancing tumor detection in PET and CT scans. By leveraging deep learning algorithms, these new AI techniques are significantly improving the accuracy and efficiency of image data analysis. Medical professionals can greatly benefit from these innovations, as they lead to quicker diagnoses and potentially better patient outcomes. The integration of AI into diagnostic processes represents a promising leap forward in the fight against cancer and other serious diseases. As the healthcare industry continues to embrace technology, the collaboration between AI and medical imaging could reshape the future of diagnostics. #ArtificialIntelligence #MedicalImaging #CancerDetection #HealthcareInnovation #DeepLearning Silver is the most Critical Mineral in the world. Learn more here: https://lnkd.in/gPXKzFfm Read more: https://lnkd.in/gjRCMC3F

Discover the groundbreaking UniMed-CLIP model, designed to revolutionize medical imaging AI! This innovative vision-language model leverages a massive 5.3 million image-text pair dataset to understand diverse medical imaging modalities like X-rays, MRIs, Ultrasounds, Pathology, and Retinal Fundus. Developed by top institutions such as MBZUAI, EPFL, and ANU, UniMed-CLIP outperforms existing proprietary models, offering a +12.61% improvement in zero-shot tasks and setting the foundation for scalable and open-source AI tools in healthcare. Stay tuned to see how this can reshape the future of diagnostics! Watch more: YouTube: https://lnkd.in/gw76SDeu Spotify: https://lnkd.in/gkZyn-x9 Read more: https://lnkd.in/g-_YEhi3

Recently, I had the opportunity to test the Gemini 2.0 Flash Experimental model, developed by Google DeepMind, in real-time while analyzing a shoulder MRI study. It was an incredible experience to see this multimodal AI in action, processing and understanding images with an ability that surprised me. Although this model is not specifically trained for medical imaging, it could interpret the study and even suggest potential diagnoses—a remarkable feat. This experience makes us reflect on the double-edged nature of such advancements. On one hand, it's natural for professionals, especially radiologists, to feel apprehensive about the potential for AI to take over some of their roles. On the other hand, I believe that tools like this can complement our expertise, allowing us to focus on more complex cases and patient care while improving efficiency and diagnostic precision. What also struck me was how this technology has the potential to democratize learning and skill development. With the guidance of a virtual tutor like this, anyone could learn and perform tasks that would have seemed inaccessible before. Testing the Gemini 2.0 Flash Experimental was a fascinating reminder of how AI is evolving to not only assist professionals, but also empower individuals across all fields. For us in healthcare and radiology, the challenge—and opportunity—is to embrace these tools as allies, shaping their integration to amplify the value we bring to patients and society. For anyone who wants to test: https://lnkd.in/dwrBDdwx And you, what do you think? Are advancements like these more of a challenge or an opportunity for your field?

🌟 Transforming Medical Diagnostics with AI-Driven Image Synthesis! 🌟 In the ever-evolving world of healthcare, the urgency for advanced diagnostic tools has never been greater. Generative AI, particularly through Diffusion Models, is revolutionizing how we approach medical imaging. 🔹 Medical Image Creation: Leveraging Diffusion Models, we can now generate high-quality images for MRI, CT scans, and X-rays, essential for training and educating healthcare professionals, especially in regions with scarce medical imaging data. 🔹 Image Enhancement: AI-enhanced imaging helps in sharpening the details of existing medical images, making it possible to diagnose diseases with unprecedented accuracy. This includes enhancing resolution and clarifying details obscured by noise. 🔹 Disease Progression Visualization: AI-generated images illustrate the stages of diseases like cancer, providing doctors and patients a clearer understanding of illness progression and treatment options. This technological advancement not only boosts diagnostic accuracy but also extends critical healthcare support to under-resourced areas, marking a significant milestone in medical technology. 💡 Explore how these AI capabilities are poised to transform patient care and diagnostic precision. Join us in our journey to make healthcare more accurate, efficient, and accessible. #HealthTech #ArtificialIntelligence #MedicalImaging #Innovation #DigitalHealth #NeuroBot