SoftHealth 365’s Post

Radiologist👩⚕️ vs. AI 🤖: Who writes the best report? BOTH, TOGETHER! At Mata, we believe AI isn't here to replace radiologists, but to empower them. By working side by side, AI and radiologists can achieve greater productivity and efficiency than ever before. How AI improves reporting: 🚀 Faster turnaround: AI streamlines the documentation process, allowing radiologists to focus more on patient care and less on paperwork. 🎯 Efficiency: Our tools help radiologists produce comprehensive, standardized reports with greater ease and speed. 🤝 Collaboration, not replacement: AI enhances productivity, helping radiologists complete their reports faster, without compromising quality. Try now: https://mata-md.com/login #AIinRadiology #Radiology #RadiologyInnovation #MedicalAI

Artificial intelligence is transforming imaging diagnostics in healthcare, revolutionizing accuracy, speed, and workflow efficiency. AI algorithms enhance precision in analyzing medical images, reducing errors, and delivering faster results crucial in surgical emergencies. They enable predictive analytics for early interventions and offer decision support to radiologists, prioritizing urgent cases. Excited to enhance our machine learning algorithm for diagnosing and recommending treatment for facial trauma patients. #Healthcare #AI #ImagingDiagnostics #PatientCare

Co-Founder and CEO of Harrison.ai Dr Aengus Tran sat down with Fierce Healthcare to share the news of the launch of Harrison.rad.1, the latest frontier in radiology-specific foundational models. It has been trained on extensive medical imaging data that is representative and diverse, enabling superior model training and accuracy to help close the radiology gap and help radiologists provide better patient care. "We are on a mission to scale the global capacity of diagnosis and treatment and improve patient outcomes by building a suite of AI tools that free doctors from doing repetitive and mundane tasks so they can truly focus on what is uniquely human," Tran said. Read more from this insightful conversation: https://bit.ly/3MvSLO7 and learn more about Harrison.rad.1: https://bit.ly/3Zce1zT #harrisonrad1 #foundationalmodel #artificialintelligence #ai

Love what Harrison.ai are working on. Rad.1 is one of the best examples of AI for Good. A beautiful gift for society to help the radiologists that support our friends and families walking through difficult chapters. In health, there are shortages on every front. In this article Dr Aengus Tran speaks of "Global healthcare is facing multiple intersecting challenges. There is a shortage of radiologists and pathologists along with rising imaging volumes and associated data per case. In the U.S. there are 11 radiologists per 100,000 people. More than two-thirds of the world’s pathologists are distributed across only 10 countries." This is an awful statistic, especially for families living in remote or regional areas. From shortages of radiologists, to major delays for home assessments due to the lack of access to Occupational Therapists. Challenges: We can't train enough people fast enough, can't overload the existing staff with more work, or remove the tyranny of distance. The areas in AI that I'm most excited about is where AI helps timely assessment at the edge, enabling early escalation, where there are labour shortages, overloaded frontline workers and inequitable access - especially for remote and regional areas. Cheering for teams crafting AI for Good tools that can change health equity access especially for people in remote regions! Dr Aengus Tran, Suneeta Mall and the teams at Harrison.ai - such great work. Cheering for your growth. Recommend different partners and teams to check out Rad.1 explore ways to integrate into health architectures and wider training systems as well too. ... 👀 😃 Curiosity notes to explore further with Partners and Friends: Michael Bainbridge ( 🤔 different capability components as you design health architectures), Lars Hyland/Carl Lavin (imagine this inline for Totara x NHS training workflows, embedded future of learning in the flow of work, vertical specific mobile AI trainer tools for different health workers, domain specific grounded health integrations instead of GIGO), Androgogic - Peter Mac Cancer x McGrath Foundation future research pilot - for end to end journey touch points? Nathan Kirchner/Rodger Watson/Distinguished Prof Jon Adams/Peter Schofield AO/Catherine Bridge Computer vision x conversational mobile interfaces with specific health domain AI agents, chained along the care journey. Jia (Jenny) Liu / Christopher J McHardy Integrated oncology multi-disciplinary team support touch points post-op to increase long tail scanning surface beyond the main hospital interaction and support earlier intervention. David Anstee - St Vincents Digital Innovation x Dr Jason V x UNSW Ageing Futures Institute x Uniting Innovation x HammondCare x Prof Chris P x Zed Technologies. Conja - workshop Spatial computing ux for Visual Health AI agents, e.g. Rad.1, computer vision inputs, conversational audio interface, dense images vision pro AR UX but on mobile.(cc: Edmund Wittich, Nathan Kirchner) #Ai4Good

Discover how to improve kidney stone detection using AI! This article guides you through fine-tuning the YOLOv10 model on a custom dataset, speeding up diagnosis drastically—from minutes per report to nearly instant results. Ideal for medical professionals and AI enthusiasts looking to push the boundaries of medical imaging technology. https://lnkd.in/g7RcYwAJ #AIinHealthcare, #MedicalImaging, #YOLOv10, #MachineLearning, #DeepLearning, #AIResearch #healthcare

hello connection As of today, AI is primarily utilized to increase speed and accuracy in the healthcare realm. Some of the current uses of AI in this field include: Diagnosing Patients: AI algorithms analyze medical imaging data, such as X-rays, MRIs, and CT scans, to assist healthcare professionals in accurate and swift diagnoses. #snsinstitutions #snsdesignthinging #designthinkers

Collaborative #AI models, like the vision–language-based Flamingo-CXR, are designed to assist professionals by generating high-quality, context-aware reports—in this case, medical radiology reports for chest X-rays. This AI model produces diagnostic narratives by analyzing medical images, creating a first draft that clinicians can then refine. In testing, Flamingo-CXR’s reports were rated as equal to or better than traditional clinician-written reports in 77.7% of cases. 🔥 By handling routine tasks and enabling quicker, data-driven insights, such AI tools allow professionals to focus on complex, nuanced cases. For industry leaders, adopting collaborative AI is a strategic move to enhance precision, optimize workflows, and drive a future where technology and human expertise work in harmony across sectors. #GenAI #healthtech

Embark on a journey through the cutting-edge landscape of AI in medical imaging! From enhancing diagnostic accuracy to optimizing patient care, artificial intelligence is revolutionizing the healthcare industry. Join the conversation as we explore the role of AI: will it supplement radiologists or potentially reshape the landscape of diagnosis? Share your insights and be part of the future of medicine on www.stethup.ai Read more about "AI - In Medical Imaging' at https://lnkd.in/dPNh-XF9 #StepUPwithStethUp #Artificialintellegence #AIinhealthcare

Enhance your diagnostic confidence with #intelligent AI-powered scan procedures using myExam Companion. Step by step, myExam Companion guides users through diagnostic #CT exams and enables consistent results — regardless of experience level, individual patient conditions, and procedure type.   Watch the interview with @Lucia La Mura, MD, from @Centro Medico Ascione Torre del Greco, Italy, and discover how myExam Companion can make your workflows more efficient: https://lnkd.in/dCx5KSFS You think ahead. We innovate ahead. #strongportfolio #AI #digital

**Day 3: AI in Medical Imaging – Boosting Accuracy One Scan at a Time! 🩻** Today, let’s dive into the amazing world of **medical imaging** powered by AI! 🌐 Did you know that some AI models can analyze X-rays, MRIs, and CT scans faster and more accurately than ever before? ⚡ One standout model in this space is **CheXpert** from Hugging Face. It can detect up to 14 lung diseases from chest X-rays with stunning precision! 🫁✨ Now, how does this align with healthcare business architecture? Well, AI solutions like CheXpert can streamline **decision-making workflows** for radiologists, speeding up diagnosis and reducing errors. As AI becomes more integrated, institutions can optimize resources across departments—time, money, and human capital get reallocated for **maximum efficiency and scalability** 📈. Simply put: **Better diagnoses, faster services, and cost savings** 💡. Do you think AI will soon lead the field of medical imaging? How are you preparing your organization's strategy to leverage these innovations? Let’s discuss! 🎤👇 #AIMedicalImaging #CheXpert #RadiologyAI #HealthcareInnovation #MachineLearningInMedicine #BusinessArchitecture #HealthTech