Stanford Medicine develops AI tool for gene-editing experiments

New AI model can identify treatments that reverse disease states in cells In a move that could reshape drug discovery, researchers at Harvard Medical School have designed an artificial intelligence model capable of identifying treatments that reverse disease states in cells. via News Medical Device / Technology News Feed

AI and machine learning are revolutionizing diagnostics in healthcare, making it possible to detect diseases sooner and more accurately. As a medical electronics student, I am truly inspired by how these advanced technologies can analyze medical images—like X-rays, MRIs, and CT scans—at remarkable speed and precision. This not only supports doctors in making faster, better decisions but can also help identify serious conditions, such as cancer and heart disease, at earlier stages when treatments are most effective. The combination of innovative medical hardware and powerful AI models represents a major step forward for patient care. Witnessing this synergy motivates me to learn more and be part of a field that is genuinely improving lives. The future of healthcare feels bright as electronics and intelligence come together to save lives and build healthier communities. #MedicalElectronics #ArtificialIntelligence #MachineLearning #HealthcareInnovation #Diagnostics #FutureOfHealthcare #StudentJourney

**Computational Biomedical Intelligence in AI-Powered Decision Support Systems** 🧠💡 This work explores how advanced computational intelligence can empower healthcare decision-making, improve diagnostic accuracy, and support clinicians with AI-driven insights. Looking forward to connecting with peers, researchers, and professionals interested in **AI, biomedical engineering, and healthcare innovation**. 🚀 \#ArtificialIntelligence #BiomedicalIntelligence #DecisionSupportSystems #HealthcareAI #Research

Scientists grow mini-brains in lab to unlock energy-efficient artificial intelligence Tiny brain models could build artificial intelligence that works faster and wastes less power. https://lnkd.in/d9k-q_Jq

Researchers Achieve 60% More Accurate Microrobot Depth Estimation for Biomedical Applications Researchers developed a new method that enables accurate three-dimensional tracking of transparent microrobots, even with limited training data, by integrating physics-based image analysis with artificial intelligence to improve their control for biomedical applications #quantum #quantumcomputing #technology https://lnkd.in/ec4tRUBY

📢 The Special Issue "New Sights of Machine Learning and Digital Models in Biomedicine" is open for submissions! 🥼 This Special Issue is guest-edited by Prof. Dr. Hatem Alhadainy. 💡 This Special Issue explores the intersection of machine learning (ML), digital modeling, and biomedicine, highlighting innovative approaches that leverage advanced computational techniques to enhance medical research, diagnosis, treatment, and patient care. 🔗 Click the link to access more details about the Special Issue: https://lnkd.in/gxs_Gybe 🕑 The deadline for manuscript submissions is 31 January 2026. 🎉 Welcome to join us as authors and reviewers! 👏 And welcome to follow our LinkedIn account @Bioengineering MDPI. #Machine_Learning #Digital_Models #Biomedicine

FROM THE LIVE SHOW: Dr. Derya Unutmaz, Professor at The Jackson Laboratory, on whether biomedical research needs specialized or generalized models. “It won’t be either/or. We need both. Specialized models like AlphaFold can tackle complex tasks — like predicting protein 3D structures or molecular dynamics — that require intense computation. But generalized models like GPT-5 bring breadth and flexibility. For now, the future of bio-AI is a combination.”

🔧 𝗔𝗜 𝗧𝗿𝗼𝘂𝗯𝗹𝗲𝘀𝗵𝗼𝗼𝘁𝘀 𝗥𝗮𝗱𝗶𝗮𝘁𝗶𝗼𝗻 𝗧𝗵𝗲𝗿𝗮𝗽𝘆 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 A fascinating new report from Practical Radiation Oncology shows clinics using Google’s Notebook LM to diagnose LINAC machine issues—with remarkable results. By feeding their own manuals and documentation into Notebook LM, physics teams created a custom AI troubleshooter that accurately identifies equipment problems on the first pass. https://lnkd.in/e36RxbmH 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) 𝗶𝗻 𝗮𝗰𝘁𝗶𝗼𝗻—combining existing AI models with clinic-specific knowledge without expensive retraining. The impact: Physics teams save significant time on machine diagnostics, letting them focus on patient care instead of equipment detective work. 𝗣𝗲𝗿𝗳𝗲𝗰𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝗵𝘂𝗺𝗮𝗻-𝗔𝗜 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Not replacing expertise, but amplifying it with institutional knowledge at AI speed. How are you using AI tools with your own data to solve specific challenges? #RadiationOncology #NotebookLM #MedicalPhysics #HealthTech #RAG #AIinHealthcare

It always amazes me truly how long it takes to communicate about innovation until solid clincal data and patient outcomes actually change daily practice and consultation pathways. Rayner’s 2025 ESCRS Symposium was no doubt the most seeked scientific event this year - no other topic but AI spiral optics and outcomes set the trend. This technology has it all: a degree of sexiness, data robustness, a degree of cheekiness to challenge the status quo and clinical outcomes to win every surgeons in a storm ❤️