# Medical imaging Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis, medical intervention, and physiological visualization. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Improving HCLS AI reasoning with open-source agent skills DevFeed: [Improving HCLS AI reasoning with open-source agent skills]() Original publisher: [Read original article]() Author: Michael Hsieh Published: 2026-09-16T19:00:00Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Agent Skills](), [Artificial Intelligence](), [foundation-models](), [Open Source](), [Bioinformatics](), [Medical imaging]() Tags: [agent-skills](), [agents](), [ai](), [ai-agents](), [amazon-bedrock](), [amazon-quick-suite](), [announcements](), [artificial-intelligence](), [foundation-models](), [generative-ai](), [healthcare](), [healthcare-and-life-sciences](), [intermediate-200](), [kiro](), [life-sciences](), [medical-imaging](), [open-source](), [strands-agents]() ### AI overview This post presents 38 open-source agent skills spanning 11 healthcare and life sciences domains. The skills encode domain decision procedures for AI agents, and the reported evaluation found a 70-86% head-to-head win rate over agents without the skills. ### Source excerpt AI agents on foundation models often misapply healthcare and life sciences decision frameworks, citing the right guideline but applying it incorrectly. This post shares 38 open-source agent skills across 11 HCLS domains that close this gap, with installation steps, three worked use cases, and a 410-prompt evaluation showing a 70-86% win rate. ## Children's Hospital of Philadelphia Uses Open Source AI and MONAI to Model Pediatric Hearts DevFeed: [Children's Hospital of Philadelphia Uses Open Source AI and MONAI to Model Pediatric Hearts]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-15T09:00:42Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [MONAI](), [Medical imaging](), [Artificial Intelligence](), [Open Source](), [Machine learning]() Tags: [ai](), [ai-for-good](), [healthcare-and-life-sciences](), [machine-learning](), [medical-imaging](), [monai](), [open-source](), [openusd](), [robotics](), [simulation](), [software]() ### AI overview Children's Hospital of Philadelphia uses open source AI tools built on MONAI to generate anatomically precise pediatric heart models from medical images in seconds. Its teams are applying machine learning to support care for children with congenital heart disease. ### Source excerpt Children's Hospital of Philadelphia is using open source AI tools to model children's hearts in seconds -- with the goal of enabling safer, more precise care for kids with congenital heart disease. ## How Clario technology detects PHI/PII in DICOM images using Amazon Bedrock DevFeed: [How Clario technology detects PHI/PII in DICOM images using Amazon Bedrock]() Original publisher: [Read original article]() Author: Alex Boudreau Published: 2026-08-19T14:29:31Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Amazon Bedrock](), [Medical imaging](), [Amazon Textract](), [Amazon Web Services](), [data]() Tags: [amazon](), [amazon-bedrock](), [amazon-textract](), [analysis](), [architecture](), [aws](), [customer-solutions](), [data](), [healthcare](), [images](), [medical-imaging](), [pii](), [technology](), [workflow]() ### AI overview Clario uses Amazon Bedrock and Amazon Textract to automate the detection of PHI and PII in DICOM image slices from clinical trials, including information stored in metadata and text embedded in image pixels. ### Source excerpt Clario, part of Thermo Fisher Scientific, uses Amazon Bedrock and Amazon Textract to automatically detect protected health information (PHI) and personally identifiable information (PII) across thousands of DICOM image slices in clinical trials, covering both metadata tags and text burned into the image pixels. ## Developing Healthcare Robotics with GPU-Native Medical Physics Simulation DevFeed: [Developing Healthcare Robotics with GPU-Native Medical Physics Simulation]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-07-28T20:49:21Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Isaac for Healthcare](), [Robotics](), [Reinforcement learning](), [GPU](), [datasets](), [Medical imaging](), [Cosmos](), [Open Source](), [real-time](), [Nvidia]() Tags: [agentic-ai-generative-ai](), [data](), [datasets](), [featured](), [gpu](), [healthcare](), [isaac](), [isaac-for-healthcare](), [isaac-sim](), [medical-imaging](), [nvidia](), [open-source](), [physical-ai](), [physics](), [real-time](), [reinforcement-learning](), [robotics](), [simulation-modeling-design](), [warp](), [world-model]() ### AI overview The article presents NVIDIA's open source, GPU-accelerated Medical Physics Simulation framework for healthcare robotics. It addresses limited medical robotics data, poor generalization, and slow development by enabling anatomical digital twins, device-anatomy and medical imaging simulation, and GPU-scale reinforcement learning within Isaac for Healthcare, Isaac Sim, and Isaac Lab. ### Source excerpt Unlike autonomous driving or industrial robotics, healthcare robotics can't rely on internet-scale data collection or unlimited real-world experimentation.... ## Next generation medical image interpretation with MedGemma 1.5 and medical speech to text with MedASR DevFeed: [Next generation medical image interpretation with MedGemma 1.5 and medical speech to text with MedASR]() Original publisher: [Read original article]() Published: 2026-01-13T20:57:16Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Medical imaging](), [AI Models](), [asr](), [Generative AI](), [Google](), [hugging face](), [Cloud](), [Kaggle]() Tags: [ai-models](), [asr](), [generative-ai](), [google](), [google-cloud](), [health](), [health-bioscience](), [healthcare](), [hugging-face](), [kaggle](), [machine-intelligence](), [medical-imaging]() ### AI overview Google Research describes MedGemma 1.5 4B, an updated open medical generative AI model with improved support for medical imaging, text, medical records, and 2D images. The article also presents MedASR, an open medical speech-to-text model for dictation that can pair with MedGemma for advanced reasoning. The models are available for research and commercial use through Hugging Face and Vertex AI, with a related medical AI hackathon on Kaggle. ### Source excerpt Generative AI ## How AI Medical Imaging Is Powering Precision Healthcare DevFeed: [How AI Medical Imaging Is Powering Precision Healthcare]() Original publisher: [Read original article]() Author: MARTIN ELIAS COSTA, AI ENGINEER @ TOPTAL Published: 2025-07-18T05:00:00Z Content type: article Language: en Sources: [Toptal Blog]() Topics: [Artificial Intelligence](), [Medical imaging](), [Computer vision](), [Deep neural networks](), [data](), [Development]() Tags: [ai](), [ai-engineer](), [artificial-intelligence](), [computer-vision](), [data](), [development](), [diagnostics](), [healthcare](), [images](), [machine-learning](), [medical-imaging](), [neural-networks]() ### AI overview This article explains how artificial intelligence is transforming medical imaging from image acquisition through diagnosis. It focuses on computer vision, neural networks, medical imaging data, and GPUs, and describes an AI system that analyzes brain MRI scans, identifies demyelinating lesions, measures brain-region volumes, classifies atrophy patterns, and integrates results into electronic health records. ### Source excerpt Artificial intelligence is revolutionizing how medical images are acquired, analyzed, and interpreted. The transformation ushers in a new era of data-driven diagnostics and faster, more personalized patient care. ## Health-specific embedding tools for dermatology and pathology DevFeed: [Health-specific embedding tools for dermatology and pathology]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-08T19:33:00Z Content type: release Language: en Sources: [Google Research]() Topics: [Medical imaging](), [Embeddings](), [Deep learning](), [Machine learning](), [Google]() Tags: [computer-vision](), [deep-learning](), [embedding](), [google](), [health](), [image-classification](), [medical-imaging](), [product](), [release]() ### AI overview Google Research announces Derm Foundation and Path Foundation, two domain-specific embedding tools for research in dermatology and digital pathology. The tools convert medical images into specialized numerical vectors that researchers can use to develop models for downstream applications. ### Source excerpt Posted by Dave Steiner, Clinical Research Scientist, Google Health, and Rory Pilgrim, Product Manager, Google Research There's a worldwide shortage of access to medical imaging expert interpretation across specialties including radiology, dermatology and pathology. Machine learning (ML) technology can help ease this burden by powering tools that enable doctors to interpret these images more accurately and efficiently. However, the development and implementation of such ML tools are often limited by the availability of high-quality data, ML expertise, and computational resources. One way to catalyze the use of ML for medical imaging is via domain-specific models that utilize deep learning (DL) to capture the information in medical images as compressed numerical vectors (called embeddings). These embeddings represent a type of pre-learned understanding of the important features in an image. Identifying patterns in the embeddings reduces the amount of data, expertise, and compute needed to train performant models as compared to working with high-dimensional data, such as images, directly. Indeed, these embeddings can be used to perform a variety of downstream tasks within the specialized domain (see animated graphic below). This framework of leveraging pre-learned understanding to solve related tasks is similar to that of a seasoned guitar player quickly learning a new song by ear. Because the guitar player has already built up a foundation of skill and understanding, they can quickly pick up the patterns and groove of a new song. Path Foundation is used to convert a small dataset of (image, label) pairs into (embedding, label) pairs. These pairs can then be used to train a task-specific classifier using a linear probe, (i.e., a lightweight linear classifier) as represented in this graphic, or other types of models using the embeddings as input. Once the linear probe is trained, it can be used to make predictions on embeddings from new images. These predictions can be