devtech.pro’s Post

🤯 Prepare to be amazed! This new A.I. tool is changing the game for doctors and expecting parents everywhere. You won't believe what it can do! Forget grainy 2D ultrasounds. MIT researchers have created Fetal SMPL, a revolutionary machine-learning tool that transforms tricky MRI scans into stunningly detailed 3D models of a fetus. 👶🔍 Why is this a big deal? Precision: It can measure things like a baby's head or abdomen size with pinpoint accuracy (within 3.1 millimeters—smaller than a grain of rice!). Insight: Doctors can now get a crystal-clear look at fetal development and spot potential abnormalities earlier than ever before. Future-proof: This technology is laying the groundwork for even more advanced tools that could one day model a baby's internal organs and track their growth over a lifetime. The future of prenatal care is here, and it's powered by A.I. This is a massive leap forward for medical imaging and a win for health and technology. #AI #MachineLearning #MedTech #HealthTech #PrenatalCare #Innovation #MIT #CSAIL #FutureOfMedicine #FetalHealth

AI + Data in Healthcare: Some thoughts AI is no longer a future concept in medicine — it’s here. In 2024, 2 out of 3 U.S. doctors said they already use AI tools, up nearly 80% from the previous year. Around 65% of hospitals use predictive models tied to patient records for decisions. AI is already helping detect conditions like diabetic retinopathy and speeding up radiology reporting. But the big question is: how do we balance efficiency with empathy? Health is personal — patients want both smart insights and human connection. I believe the real value of AI in MedTech will come when we integrate diverse data (labs, images, notes) while keeping trust, transparency, and patient experience at the center. #AI #Healthcare #MedTech #DataIntegration

Last year, the NHS deployed an AI tool across every stroke centre in England, designed to rapidly analyse CT scans. By automating the initial scan review, the tool aims to speed up diagnosis, reduce human error, and ensure that patients receive timely care. Early reports indicate that compared with manual review: Average time-to-treatment has dropped from 140 minutes to 79 minutes. The proportion of patients making a full recovery has nearly tripled – from 16% to 48%. Trained on thousands of imaging datasets, the model uses image texture analysis and pattern recognition to detect subtle variations in lesions and brain tissue that may be missed or take longer for human radiologists to interpret. The technology estimates the presence and severity of a stroke as well as the time since stroke onset, the potential reversibility of brain tissue damage, and helps determine the most appropriate treatment. Over 100,000 people are affected by stroke in the UK every year, meaning we likely all have, or will have direct experience of how quickly it can change lives. I'm grateful to see the growing maturity of AI in clinical medicine, and fascinated by how quickly early pattern recognition algorithms have moved through to increasingly sophisticated inference about tissue states and treatment options, to measurable real-world benefits in outcomes and efficiency. #EvoMedica #ModernMedComms #AI #AIinHealthcare #MedicalCommunications #MedComms #Pharma #LifeSciences #MedTech #Biotech #HealthTech #LLMs #ScientificWriting #DigitalHealth

PATIENT BENEFITS OF AI IN MEDICAL IMAGING At the heart of every medical innovation is the patient — and AI in imaging is no exception. Beyond helping radiologists, AI directly improves the patient experience in powerful ways: ✨ Faster Results – Automated analysis speeds up reporting, so patients spend less time waiting anxiously for answers. ✨ Earlier Detection – Subtle abnormalities (like tiny tumors or early lung disease) can be flagged sooner, giving patients a better chance at successful treatment. ✨ Reduced Scan Times – AI-enhanced MRI and CT scans are quicker, meaning less discomfort and less exposure to radiation when applicable. ✨ Improved Access – Portable AI-powered imaging devices make high-quality diagnostics possible in remote and underserved areas. ✨ Personalized Care – Combining imaging with AI-driven data insights leads to treatments tailored to each patient’s unique needs. AI in imaging isn’t just about technology — it’s about delivering faster, safer, and more accurate care to people who need it most. 👉 In the next post, we’ll wrap up the series with a summary and vision of how AI + medical imaging will continue to shape the future of healthcare. #AI #MedicalImaging #Radiology #HealthcareInnovation #PatientCare #FutureOfMedicine #AIinHealth

China has officially launched the world’s first AI-powered hospital, Agent Hospital. Developed by Tsinghua University, this isn’t a chatbot or add-on tool. It’s a fully integrated system with: • 42 AI doctors across 21 specialties • Coverage of 300+ diseases • Ability to treat 10,000 patients with over 90% accuracy in just days Unlike traditional deployments, Agent Hospital creates a closed-loop ecosystem where AI doctors train on half a million synthetic patient cases, refining diagnosis and treatment continuously. From ophthalmology to radiology, these AI systems are already assisting with diagnostics, streamlining workflows, and lowering barriers to care. Beyond clinical practice, they’re also training the next generation of AI-collaborative physicians. This marks the beginning of a new era where healthcare + AI converge to redefine how hospitals operate. #AI #Healthcare #Innovation #MedTech #FutureOfMedicine #ArtificialIntelligence #DigitalHealth #AIinHealthcare #TechInnovation #GlobalHealth.

🚀 AI is transforming the medical device industry at record speed. From the first AI-enabled imaging tools in the 1990s to today’s adaptive, real-time algorithms, AI has redefined what’s possible in diagnostics, personalized care, and workflow automation. 📈 As of June 2024, there are 950 FDA-approved AI-enabled devices—with radiology leading the way. And this is only the beginning. In our latest blog, we explore: 🔹 The rapid adoption of AI in medical devices 🔹 Breakthroughs in diagnostics, treatment, and patient care 🔹 How radiology, cardiology, and neurology are paving the path 🔹 Real-world applications, from reduced radiology reporting time to surgical innovation 🔹 How Segmed’s diverse, regulatory-grade imaging datasets are powering the next wave of AI-driven medical devices 👉 Read the full article here: https://hubs.li/Q03F1L7k0 At Segmed, we’re proud to support the future of healthcare by providing researchers and developers with 100M+ high-quality medical images from around the globe, helping AI models become more accurate, equitable, and impactful. #MedicalDevices #AI #MachineLearning #HealthcareInnovation #TeamSegmed

Medical imaging is a cornerstone of diagnosis, but traditional analysis by human experts can be slow and prone to variability. In 2025, artificial intelligence (AI) is revolutionizing the field with remarkable, life-saving results. Research led by the Universität zu Lübeck in Germany demonstrated that using AI in mammography screening boosts cancer detection by 17.6%, according to findings from their large-scale study of over 460,000 women published this January, while also reducing unnecessary patient recalls and leading to faster, more confident diagnoses. AI excels at identifying subtle patterns the human eye might miss. For instance, AI models now detect critical stroke-related hemorrhages on CT scans with 98.7% sensitivity, drastically cutting time to diagnosis in emergencies. The technology also extends to predictive analytics, forecasting disease progression for conditions like multiple sclerosis with over 80% accuracy. This transformation brings key systemic benefits: 1. Alleviating radiologist shortages by automating repetitive tasks Studies project that by 2025, AI could handle up to 50% of a radiologist’s workload, freeing experts for complex cases. 2. Improving accessibility through portable AI-powered systems Low-field MRI units such as Hyperfine | AI-Powered Portable MRI’s Swoop deliver diagnostic imaging at around 80% lower cost and can operate in underserved areas without traditional infrastructure. 3. Reducing healthcare costs through greater efficiency AI-enabled imaging workflows and digital pathology are projected to save institutions up to USD 12 million over five years. Reflecting its rapid, worldwide adoption, market projections show explosive growth for AI in medical imaging. The field is now moving beyond just sharper images to providing richer insights, integrating diverse health data to enable truly personalized medicine and a more patient-centric future. #AI #HealthTech #Innovation #Technology #Biotech #MedicalImaging #Diagnosis #Healthcare

I attended the Pre Placement Talk by Sumptuous Data Sciences today and it was a very insightful session. It gave me a clear idea of how data science is being applied in different industries and the kind of opportunities it can open up. I also went through the IIT Bombay Techfest newsletter where they shared about the role of AI in medical imaging and how it is already helping in areas like early cancer detection and faster diagnosis. Connecting these two experiences, I strongly feel that with the pace at which artificial intelligence is progressing, the revolution in the medical field is not far away, it is very close. #AI #Healthcare #DataScience #MedicalImaging #Innovation #ArtificialIntelligence #HealthTech

AI and healthtech are two areas where innovation directly touches lives. From early diagnosis to improved treatment planning, technology is no longer just supporting healthcare — it’s transforming it. What excites me most about these conversations is how they bring together innovation with responsibility, showing us the real impact tech can have when applied to challenges that truly matter. It’s great to see platforms encouraging such discussions — like the recent one by Techfest, IIT Bombay— that spark curiosity and awareness around how AI can shape the future of healthcare.

Quick references like this have guided clinicians for years. They help us systematically assess rhythm, rate, blocks, intervals, axis, and ischemia. These frameworks train the human mind to structure complexity and reduce error. AI now offers a parallel pathway. Algorithms can measure with precision, highlight subtle changes, and scale interpretation across millions of ECGs. Yet, as this reference highlights, the clinician’s judgment is essential. Computers excel at calculation, but context, pattern recognition, and decision making remain human strengths. The opportunity lies in combining structured clinical reasoning with AI’s analytical capacity. When the two align, the result is faster, safer, and more consistent care. Follow Zain Khalpey, MD, PhD, FACS for more on Ai & Healthcare. Image ref: Zaven Sargsyan #AIinHealthcare #Cardiology #MedicalEducation #AI #FutureOfMedicine #ECG #ClinicalDecisionSupport #HealthcareInnovation #AIandDoctors #CardiovascularHealth #DigitalHealth #MachineLearning #MedicalAI #AIIntegration #PhysicianLeadership #PatientCare #HealthcareTechnology #MedTech #AIForGood #MedicalTraining