medical-imaging

Nature Communications, Published online: 12 June 2026; doi:10.1038/s41467-026-74207-5 The study shows that multimodal medical AI models often rely heavily on text to interpret images and make predictions. As a result, they are highly susceptible to misinterpretation when the text is inaccurate.

A newly developed portable, point-of-care PET technology can image any organ, delivering high-quality results to guide interventional procedures.

No standardized, comprehensive approach for radiological image interpretation exists among medical schools in the United States of America. In order to rectify this, we outline a model aimed to help improve how medical imaging can be taught in medical schools. The VOID (Visualization, Orientation/Observation, Identification, Description) guide is a proposed universal, stepwise approach to medical…

BackgroundVision models for medical imaging often require tens of millions of parameters, raising questions about whether architectural efficiency can be achieved without sacrificing classification performance. We introduce MedLiT-seed (2.1 Million parameters) and MedLiT-nano (0.75 Million parameters), two ultra-lightweight vision transformers designed for efficient and scalable medical image ana…

BackgroundExplainable Artificial Intelligence (XAI) has emerged as a strategy to enhance the transparency and interpretability of AI systems in medical imaging. Although numerous methods have been developed to generate explanations of model behavior, their evaluation has predominantly relied on technical performance metrics rather than clinician-centered assessment. The limited involvement of cli…

MRI coils, the specialised antennas that sit close to the body and capture the radiofrequency signals that become diagnostic images, have long been a bottleneck in clinical imaging. They are rigid, expensive, and built to a one-size-fits-all standard that works reasonably well for adults but poorly for children, infants, and anyone requiring imaging of moving…

BackgroundConvolutional neural networks (CNNs) have achieved remarkable success in medical image analysis, including Alzheimer’s disease (AD) classification. However, conventional convolution operations rely on fixed sampling patterns, and most existing attention mechanisms primarily focus on feature responses while neglecting spatial sampling geometry, limiting their ability to capture structura…

BackgroundEndometriosis is a common gynecologic condition in which pelvic MRI plays an important role in diagnosis and preoperative assessment. AI-enabled automated uterus segmentation on pelvic MRI could support endometriosis care by enabling standardized volumetric measurements and quantitative imaging analyses. However, developing robust AI models for this task is challenging because endometri…

IntroductionMulti-modal image registration leverages complementary information from diverse imaging sources to achieve precise spatial alignment. However, aligning visible (VIS), near-infrared (NIR), and thermal (TH) modalities remains challenging due to appearance differences and limited annotated datasets.MethodsThis study proposes a ResU-Net-inspired framework combining heatmap prediction and …

High‑quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by data scarcity, privacy restrictions,...

Meningiomas are the most common brain cancers arising from the protective soft tissue cover of the brain called the meninges.

IntroductionThe process of precise structural evaluation for paranasal sinuses based on CT scan data establishes a foundation for medical professionals to assess human anatomical variations, supporting the diagnosis and treatment of ear, nose, and throat (ENT) conditions. Existing deep learning methods face difficulties in analyzing complex sinus structures due to limited annotated datasets and l…

Breast cancer is the second most common cancer among women in the United States today. Now, advanced technologies originally developed at the DOE's Thomas Jefferson National Accelerator Facility (Jefferson Lab) for studying the tiniest particles inside matter have been adapted to aid doctors in diagnosis and treatment of breast cancer patients.

Today we schedule a medical imaging scan in a clinic, doctor’s office, or at a hospital. What if we could self-monitor with a wearable unit? Researchers are working towards enabling practical wearable units and have taken two key steps to making this a reality. The post Medical imaging in a wearable form: monitoring health on the go first appeared on Acoustics.org .