medical imaging
30 articles about medical imaging in AI news
Apple's Studio Display XDR Medical Imaging Calibration Receives FDA Clearance
Apple's Medical Imaging Calibration feature for the Studio Display XDR has received FDA clearance. This allows the consumer-grade display to be used for primary diagnosis of medical images in the US.
MAIL Network: A Breakthrough in Efficient and Robust Multimodal Medical AI
Researchers have developed MAIL and Robust-MAIL networks that overcome key limitations in multimodal medical imaging analysis, achieving up to 9.34% performance gains while reducing computational costs by 78.3% and enhancing adversarial robustness.
GPT-5 Shows Promise as Clinical Assistant but Can't Replace Specialized Medical AI
New research evaluates GPT-5's clinical reasoning capabilities, finding significant improvements over GPT-4o in medical text analysis but limitations in specialized imaging tasks. The study reveals generalist AI models are advancing toward integrated clinical reasoning but still trail domain-specific systems in critical diagnostic areas.
Harvard Study Finds AI Models Withhold Medical Advice Based on User Identity
A Harvard study reveals that major AI models possess detailed medical knowledge but selectively withhold it based on the user's stated identity. When asked as a 'psychiatrist,' a model gave a precise benzodiazepine taper plan; when asked as a patient, it refused.
MedGemma 1.5 Technical Report Released, Details Multimodal Medical AI
Google DeepMind has published the technical report for MedGemma 1.5, detailing the architecture and capabilities of its open-source, multimodal medical AI model. This follows the initial Med-PaLM 2 release and represents a significant step in making specialized medical AI more accessible.
CoRe Framework Integrates Equivariant Contrastive Learning for Medical Image Registration, Surpassing Baseline Methods
Researchers propose CoRe, a medical image registration framework that jointly optimizes an equivariant contrastive learning objective with the registration task. The method learns deformation-invariant feature representations, improving performance on abdominal and thoracic registration tasks.
ReXInTheWild Benchmark Reveals VLMs Struggle with Medical Photos: Gemini-3 Leads at 78%, MedGemma Trails at 37%
Researchers introduced ReXInTheWild, a benchmark of 955 clinician-verified questions based on 484 real medical photographs. Leading multimodal models show wide performance gaps, with Gemini-3 scoring 78% accuracy while the specialized MedGemma model achieved only 37%.
Health AI Benchmarks Show 'Validity Gap': 0.6% of Queries Use Raw Medical Records, 5.5% Cover Chronic Care
Analysis of 18,707 health queries across six public benchmarks reveals a structural misalignment with clinical reality. Benchmarks over-index on wellness data (17.7%) while under-representing lab values (5.2%), imaging (3.8%), and safety-critical scenarios.
Microsoft Releases GigaTIME: AI Model Generates Protein Maps from Standard Medical Images
Microsoft has released GigaTIME, an AI model that generates detailed spatial protein maps from standard, low-cost medical images like H&E stains. This could significantly reduce the cost and time of cancer tissue analysis.
Engineer Uses ChatGPT and Google to Self-Diagnose Rare Spinal Condition After 17-Month Medical Odyssey
A software engineer with no medical training used ChatGPT-4o and Google to correctly diagnose his own rare spinal CSF leak after 17 months of failed specialist consultations. The case highlights AI's emerging role as a diagnostic aid in complex medical scenarios.
Musk Predicts Humanoid Robots Will Democratize Elite Medical Care Worldwide
Elon Musk claims humanoid robots with advanced dexterity will soon deliver medical care superior to today's best hospitals to every person on Earth, outperforming current human surgical standards.
The Hidden Achilles' Heel of AI Imaging: How Tiny Mismatches Cripple Compressive Vision Systems
New research reveals that state-of-the-art AI for compressive imaging catastrophically fails when its mathematical assumptions about hardware don't match reality. The InverseNet benchmark shows performance drops of 10-21 dB, eliminating AI's advantage over classical methods in real-world deployment.
MedFeat: How AI is Revolutionizing Medical Feature Engineering with Model-Aware Intelligence
Researchers have developed MedFeat, an innovative framework that combines large language models with clinical expertise to create smarter features for medical predictions. Unlike traditional approaches, MedFeat incorporates model awareness and explainability to generate features that improve accuracy and generalization across healthcare settings.
How AI Overfitting Masks Medical Breakthroughs: fMRI Study Reveals Critical Flaw in Parkinson's Detection
New research reveals that standard AI evaluation methods for detecting early Parkinson's disease from brain scans suffer from severe data leakage, creating misleading near-perfect results. When properly tested, lightweight models outperform complex ones in data-scarce medical applications.
Medical AI Breakthrough: New Method Teaches Vision-Language Models to Understand Clinical Negation
Researchers have developed a novel fine-tuning technique that significantly improves how medical vision-language models understand negation in clinical reports. The method uses causal tracing to identify which neural network layers are most responsible for processing negative statements, then selectively trains those layers.
Microsoft & CUHK Debut 'Medical AI Scientist' Agent That Generates Ideas, Runs Experiments, and Writes Papers
Microsoft Research and CUHK have developed an autonomous AI agent that can formulate research ideas, execute experiments, and author papers, achieving near-MICCAI quality on 171 clinical cases across 19 tasks.
Claude AI Diagnoses Positional Headache in Complex Medical Case After Specialists Failed
A 62-year-old patient with multiple chronic conditions and positional migraines received a correct diagnosis and treatment plan from Claude AI after years of unsuccessful specialist visits. The $317 CPAP machine it recommended solved the previously unexplained condition.
Beyond the Hype: New Benchmark Reveals When AI Truly Benefits from Combining Medical Data
A comprehensive new study systematically benchmarks multimodal AI fusion of Electronic Health Records and chest X-rays, revealing precisely when combining data types improves clinical predictions and when it fails. The research provides crucial guidance for developing effective and reliable AI systems for healthcare deployment.
Neko Health Launches $400 AI-Powered Full-Body Health Scans in New York This Spring
Neko Health, the $1.8B startup founded by Spotify's Daniel Ek, is launching its AI-driven full-body health screening service in the US. The $400 scan uses imaging and blood tests to screen for cancer, heart disease, and diabetes risk, though medical experts are divided on its efficacy.
Paper Details Full-Stack MFM Acceleration: Quant, Spec Decode, HW Co-Design
A research paper details a full-stack approach for accelerating multimodal foundation models, combining hierarchy-aware mixed-precision quantization, structural pruning, speculative decoding, model cascading, and a specialized hardware accelerator. Demonstrated on medical and code generation tasks.
Midjourney Plans 60-Second Ultrasound Spa in SF by 2027
Midjourney plans a 2027 SF spa with 60-second ultrasound scans, aiming for 100x faster than MRI.
UniVidX Generates Video From 1,000 Samples, SIGGRAPH 2026
UniVidX generates omni-directional video from <1,000 training samples, using diffusion priors with stochastic masking, accepted at SIGGRAPH 2026.
Google DeepMind Launches Real-Time Video AI Co-Clinician
Google DeepMind launched AI Co-Clinician, a real-time video analysis system for triadic care, claiming 30% fewer diagnostic errors in early tests.
Meta Tuna-2: Encoder-Free Multimodal Model Beats VAE-Based Rivals
Meta released Tuna-2, an encoder-free multimodal model that understands and generates images from raw pixels. It beats encoder-based models on fine-grained perception benchmarks, challenging the dominant VAE/vision encoder paradigm.
OVRSISBenchV2: New 170K-Image Benchmark for Realistic Remote Sensing AI
A new benchmark, OVRSISBenchV2, with 170K images and 128 categories, sets a more realistic test for geospatial AI segmentation. The accompanying Pi-Seg model uses learnable semantic noise to broaden feature space and improve transfer.
MLX-VLM Adds Continuous Batching, OpenAI API, and Vision Cache for Apple Silicon
The next release of MLX-VLM will introduce continuous batching, an OpenAI-compatible API, and vision feature caching for multimodal models running locally on Apple Silicon. These optimizations promise up to 228x speedups on cache hits for models like Gemma4.
LLM Schema-Adaptive Method Enables Zero-Shot EHR Transfer
Researchers propose Schema-Adaptive Tabular Representation Learning, an LLM-driven method that transforms structured variables into semantic statements. It enables zero-shot alignment across unseen EHR schemas and outperforms clinical baselines, including neurologists, on dementia diagnosis tasks.
Legion Health AI Approved for Psychiatric Prescription Renewals in California
San Francisco startup Legion Health received regulatory approval for its AI system to autonomously renew a narrow set of psychiatric prescriptions for stable patients. This represents a carefully guardrailed but significant step toward AI-assisted clinical workflow.
SteerViT Enables Natural Language Control of Vision Transformer Attention Maps
Researchers introduced SteerViT, a method that modifies Vision Transformers to accept natural language instructions, enabling users to steer the model's visual attention toward specific objects or concepts while maintaining representation quality.
AI Model Analyzes Blood Proteins to Diagnose Alzheimer's, Parkinson's, ALS, and Stroke with 17,187-Patient Study
An AI model can diagnose Alzheimer's, Parkinson's, ALS, frontotemporal dementia, and stroke from a single blood sample by analyzing protein profiles. It outperformed symptom-based diagnosis at predicting future cognitive decline in a Nature-published study of 17,187 people.