Kelyon’s Post

𝗔𝗱𝘃𝗮𝗻𝗰𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝘄𝗶𝘁𝗵 𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗛𝗔𝗜-𝗗𝗘𝗙 🏥💻 Developing AI models for healthcare presents significant challenges – 𝗹𝗶𝗺𝗶𝘁𝗲𝗱 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗱𝗮𝘁𝗮, 𝘁𝗵𝗲 𝗻𝗲𝗲𝗱 𝗳𝗼𝗿 𝗲𝘅𝘁𝗲𝗻𝘀𝗶𝘃𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀, and 𝘁𝗵𝗲 𝗵𝗶𝗴𝗵 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗲𝘅𝗽𝗲𝗿𝘁 𝗱𝗮𝘁𝗮 𝗹𝗮𝗯𝗲𝗹𝗶𝗻𝗴. To address these challenges, 𝗚𝗼𝗼𝗴𝗹𝗲 𝗵𝗮𝘀 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲𝗱 𝗛𝗲𝗮𝗹𝘁𝗵 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 (𝗛𝗔𝗜-𝗗𝗘𝗙) – a suite of 𝗼𝗽𝗲𝗻-𝘄𝗲𝗶𝗴𝗵𝘁 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 and supporting resources that empower developers to build AI solutions with 𝗳𝗲𝘄𝗲𝗿 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗮𝗻𝗱 𝗵𝗶𝗴𝗵𝗲𝗿 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆. 🌐 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 𝗺𝗼𝗿𝗲 about how HAI-DEF can support your AI healthcare projects and 𝗱𝗿𝗶𝘃𝗲 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗽𝗮𝘁𝗶𝗲𝗻𝘁 𝗰𝗮𝗿𝗲. https://lnkd.in/dEda9Mkr #AIinHealthcare #MachineLearning #GoogleHealth #MedicalAI #Innovation Solvership Università degli Studi di Napoli Federico II Sveučilište Algebra Università di Pavia Griffith College Dublin Smion Univerzitet Crne Gore Kelyon Jozef Stefan Institute Royal College of Surgeons in Ireland (RCSI)

AI will make us healthier. One of the most impactful opportunities for AI in the near-term is with medical imaging. It will improve care through better image analysis, reduced workloads, and clinical trials recruitment. Today's announced partnership between Microsoft and Mass General Brigham and University of Wisconsin-Madison to create medical imaging copilots is a massive step in that direction. https://lnkd.in/g2sPmtMi

🔬 Breakthrough in Medical Imaging AI: UCLA’s SLIViT Model Researchers at UCLA have unveiled SLIViT (SLice Integration by Vision Transformer), an advanced AI model designed to analyze 3D medical scans like MRIs, CTs, and ultrasounds with unprecedented efficiency and precision. 🚀 Key Highlights: • Lightning-fast Analysis: SLIViT achieves expert-level accuracy 5,000x faster than traditional human analysis. • Minimal Data Requirement: Unlike other models, SLIViT only needs hundreds of training samples, making it practical for real-world clinical use. • Innovative Transfer Learning: By leveraging 2D medical data, SLIViT can quickly learn and adapt to 3D datasets, even when they’re limited. 💡 Why This Matters: As the demand for faster diagnostics grows, SLIViT’s ability to deliver high-accuracy imaging analyses in record time could revolutionize healthcare. The model’s low data requirements make it especially valuable for providers with limited resources, helping bring top-tier imaging capabilities to more communities. #HealthTech #AIBreakthrough #MedicalImaging #HealthcareInnovation #UCLAResearch #Diagnostics #MachineLearning #TransferLearning #MedTech #FutureOfHealthcare https://lnkd.in/gAhTmvqd

🧠 Unlocking Clarity in Medical Imaging: Tyche AI's Innovative Approach 🎯 In the intricate world of medical imaging, where clarity often remains elusive, Tyche AI emerges as a game-changer. Developed by a collaborative team from MIT, the Broad Institute, and Massachusetts General Hospital, Tyche is a pioneering AI tool designed to capture the inherent ambiguity in medical image segmentation. By offering multiple plausible segmentations, Tyche allows clinicians to explore different interpretations of a single image, enhancing decision-making in critical healthcare scenarios. This revolutionary tool could significantly refine diagnostic processes and accelerate medical research by acknowledging and utilizing the often-overlooked subtleties and uncertainties inherent in clinical images. Discover how Tyche AI is setting new standards in precision medicine, ready to transform patient outcomes by embracing the complexity of human health. #MedicalAI #Innovation #HealthTech #TycheAI

Great Summary about #AI in #LifeSciences & #Healthcare by Eric Topol, MD, issued a few weeks ago:   👉 Hippocratic AI released the first safety-focused #LLM for real-time patient conversations. Topol shares details about how that LLM operates   👉 AI-enabled discovery of antibiotics 💊, with a first new class in decades   👉 Pathology 🔬 LLMs: a step towards general-purpose pathology unimodal model   👉 Radiology 🎞 & AI: incremental performance support with AI is highly variable and unpredictable   👉 AI Health Misinformation 🤥: 4 LLM assessed and show differences across models. Overall this confirms potential for LLMs to propagate health misinformation.   🎤 Topol’s conclusion: “We can't get to A.I.-powered healthcare without compelling prospective clinical trial evidence in the real world of medical care, with diverse participants. That’s what we’re missing now, and hopefully will start to see soon.”   https://lnkd.in/gC_x6vX6

Researchers at UCLA just developed SLIViT, a new AI model that can analyze complex 3D medical scans with expert-level accuracy in a fraction of the time required by human specialists. The Details: • SLIViT (SLice Integration by Vision Transformer) efficiently analyzes 3D imaging types, including MRIs, CT scans, and ultrasounds. • Matches clinical expert accuracy while reducing analysis time by a factor of 5,000. • Requires only hundreds of training samples, making it practical for real-world applications. • Leverages transfer learning, using prior knowledge from 2D medical data for efficient training with smaller 3D datasets. Why It Matters: With the growing demand for faster diagnostics, SLIViT’s ability to rapidly and accurately analyze imaging offers a potential game-changer for healthcare. The model’s capability to work with small datasets also makes it more accessible for providers with limited resources, potentially democratizing expert medical imaging. #AI #Healthcare #MedicalInnovation #UCLA #SLIViT

𝗝𝘂𝘀𝘁 𝗨𝗻𝗱𝗲𝗿 𝗢𝗻𝗲 𝗠𝗼𝗻𝘁𝗵 𝘁𝗼 𝗚𝗼! 🌟 Don’t miss this exciting PhD opportunity with the CHAI Hub, Canon Medical Research Europe Ltd. and University of Edinburgh School of Engineering! If you're passionate about AI in healthcare and skilled in frameworks like PyTorch and TensorFlow, this could be your chance to make a real-world impact. 𝗔𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: 🔍 𝗙𝗼𝗰𝘂𝘀: Developing causal AI solutions to transform how we approach medical imaging. 🌟 𝗦𝘂𝗺𝗺𝗮𝗿𝘆:  Medical images can vary due to factors like modality (CT, MRI, X-Ray) and scanner properties. These variations create challenges for human experts and AI alike. By modelling causal relationships, we aim to create robust, deployable AI solutions that truly make a difference in healthcare. 📍 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Based at the University of Edinburgh with regular visits to Canon Medical’s R&D center in Edinburgh. 🎓 This is a unique opportunity to join Sotirios (Sotos) Tsaftaris Group and be part of cutting-edge research at the CHAI Hub and Canon Medical. 👉 𝗛𝗼𝘄 𝘁𝗼 𝗔𝗽𝗽𝗹𝘆: Learn more and apply here: https://lnkd.in/ezsRUz2A ⏳ Don’t wait—apply now and take the next step in advancing AI for healthcare! #PhDOpportunity #AI #MedicalImaging #CausalAI #CHAIHub #CanonMedical #HealthcareInnovation

Exciting advancements in healthcare technology! MIT has introduced Tyche, a groundbreaking AI method that captures uncertainty in medical images. The ability to assess uncertainty in medical imaging is crucial for accurate diagnoses, allowing healthcare professionals to make informed decisions. As we move towards a future where healthcare is more accessible and efficient, technologies like Tyche are essential. They not only improve patient outcomes but also empower clinicians with the tools they need to deliver exceptional care. Together, we can reshape the healthcare landscape for the better! 🏥 #HealthcareInnovation #MIT #AI #MedicalImaging #FutureOfHealthcare https://lnkd.in/gzXYrA2M

Google Research has launched Health AI Developer Foundations (HAI-DEF), an initiative to democratise AI development in healthcare through open-weight models. This game-changing suite supports researchers, developers, and clinicians in building impactful healthcare applications. 🏥💡 🚀 What’s included in the inaugural release? 1️⃣ CXR Foundation: Specialised for chest X-rays, empowering radiology applications. 2️⃣ Derm Foundation: Tailored for analysing skin images, advancing dermatology research. 3️⃣ Path Foundation: Optimised for digital pathology, enhancing cancer detection and diagnosis. 💡 Accessible Innovation: Available on the Medical AI Research Foundations platform, these open-source models are designed to accelerate research while adhering to high standards. This marks a significant step toward democratising AI for collaborative, scalable, and ethical healthcare AI solutions. #HealthAI #MedicalImaging #OpenAI #MedTech #AIForGood #HealthcareInnovation #Collaboration

𝗙𝗮𝘀𝗰𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗻𝗲𝘄 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗲𝗹𝗱 𝗼𝗳 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗶𝗺𝗮𝗴𝗶𝗻𝗴! - 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀 𝗼𝗻 𝘁𝗵𝗶𝘀 𝗻𝗲𝘄 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 ? 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗲𝗻𝘃𝗶𝘀𝗶𝗼𝗻 𝗶𝘁 𝗶𝗺𝗽𝗮𝗰𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗶𝗺𝗮𝗴𝗶𝗻𝗴 ? https://lnkd.in/ehvKX6TW Traditionally, medical imaging analysis has focused on providing a single, definitive interpretation. However, this approach can overlook crucial subtleties. Tyche addresses this limitation by generating multiple possible interpretations of an image, effectively acknowledging the inherent uncertainty present. This method leverages machine learning to create various segmentations of an image, offering a more comprehensive picture. Additionally, Tyche is remarkably adaptable, being applicable to a wide range of medical imaging tasks without the need for retraining. This breakthrough holds immense promise for enhancing accuracy and precision in medical diagnoses. By incorporating uncertainty into the analysis, clinicians can make more informed decisions, ultimately leading to better patient outcomes. #medicalimaging #machinelearning #ai #healthcare