34 articles tagged with #medical-imaging. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
AIBullisharXiv β CS AI Β· 6d ago7/10
π§ DosimeTron, an agentic AI system powered by GPT-5.2, automates personalized Monte Carlo radiation dosimetry calculations for PET/CT medical imaging. Validated on 597 studies across 378 patients, the system achieved 99.6% correlation with reference dosimetry calculations while processing each case in approximately 32 minutes with zero execution failures.
π§ GPT-5
AINeutralarXiv β CS AI Β· Mar 177/10
π§ Researchers identified that medical multimodal large language models (MLLMs) fail primarily due to inadequate visual grounding capabilities when analyzing medical images, unlike their success with natural scenes. They developed VGMED evaluation dataset and proposed VGRefine method, achieving state-of-the-art performance across 6 medical visual question-answering benchmarks without additional training.
AIBullisharXiv β CS AI Β· Mar 117/10
π§ Researchers have developed Meissa, a lightweight 4B-parameter medical AI model that brings advanced agentic capabilities offline for healthcare applications. The system matches frontier models like GPT in medical benchmarks while operating with 25x fewer parameters and 22x lower latency, addressing privacy and cost concerns in clinical settings.
π§ Gemini
AIBullisharXiv β CS AI Β· Mar 97/10
π§ Researchers developed AIRT, an AI-powered radiation therapy planning system that generates complete prostate cancer treatment plans in under one second using deep learning. The system processes CT scans and anatomical data to produce clinically-viable radiation treatment plans 100x faster than current methods, demonstrating non-inferiority to existing commercial solutions.
π’ Nvidia
AIBullisharXiv β CS AI Β· Mar 57/10
π§ Researchers developed MPFlow, a new zero-shot MRI reconstruction framework that uses multi-modal data and rectified flow to improve medical imaging quality. The system reduces tumor hallucinations by 15% while using 80% fewer sampling steps compared to existing diffusion methods, potentially advancing AI applications in medical diagnostics.
AIBullisharXiv β CS AI Β· Mar 56/10
π§ Researchers developed a new AI framework using Unpaired Neural SchrΓΆdinger Bridge to enhance ultra-low field MRI scans (64 mT) to match the quality of high-field 3T MRI scans. The method combines diffusion-guided distribution matching with anatomical structure preservation to improve medical imaging accessibility while maintaining diagnostic quality.
AIBullisharXiv β CS AI Β· Mar 57/10
π§ Researchers propose Volumetric Directional Diffusion (VDD), a new AI method for medical image segmentation that addresses uncertainty in 3D lesion analysis. VDD anchors generative models to consensus priors to maintain anatomical accuracy while capturing expert disagreements, achieving state-of-the-art uncertainty quantification on multiple medical datasets.
AIBullisharXiv β CS AI Β· Mar 57/10
π§ Stanford researchers introduced Merlin, a 3D vision-language foundation model for analyzing abdominal CT scans that processes volumetric medical images alongside electronic health records and radiology reports. The model was trained on over 6 million images from 15,331 CT scans and demonstrated superior performance compared to existing 2D models across 752 individual medical tasks.
AINeutralarXiv β CS AI Β· Mar 57/10
π§ Researchers have released ERDES, the first open-access dataset of ocular ultrasound videos for detecting retinal detachment and macular status using machine learning. The dataset addresses a critical gap in automated medical diagnosis by enabling AI models to classify retinal detachment severity, which is essential for determining surgical urgency.
AIBullisharXiv β CS AI Β· Mar 56/10
π§ Researchers developed an automated AI pipeline for detecting cervical spine fractures in medical imaging using a novel 2D-to-3D projection approach. The system achieved clinically relevant performance comparable to expert radiologists while reducing computational complexity through optimized 2D projections instead of traditional 3D methods.
AIBullisharXiv β CS AI Β· Mar 46/102
π§ Researchers developed GTDoctor, an AI model for diagnosing gestational trophoblastic disease that achieves over 91% precision in lesion detection. The system reduces diagnostic time from 56 to 16 seconds per case while maintaining 95.59% positive predictive value in clinical trials.
AIBullisharXiv β CS AI Β· Feb 277/104
π§ Researchers developed PathVis, a mixed-reality platform for Apple Vision Pro that revolutionizes digital pathology by allowing pathologists to examine gigapixel cancer diagnostic images through immersive visualization and multimodal AI assistance. The system replaces traditional 2D monitor limitations with natural interactions using eye gaze, hand gestures, and voice commands, integrated with AI agents for computer-aided diagnosis.
AIBullisharXiv β CS AI Β· 6d ago6/10
π§ Researchers introduce Nirvana, a Specialized Generalist Model that combines broad language capabilities with domain-specific adaptation through task-aware memory mechanisms. The model achieves competitive performance on general benchmarks while reaching lowest perplexity across specialized domains like biomedicine, finance, and law, with practical applications demonstrated in medical imaging reconstruction.
π’ Hugging Faceπ’ Perplexity
AIBullisharXiv β CS AI Β· Mar 176/10
π§ Researchers introduce EviAgent, a new AI system for automated radiology report generation that provides transparent, evidence-driven analysis. The system addresses key limitations of current medical AI models by offering traceable decision-making and integrating external domain knowledge, outperforming existing specialized medical models in testing.
AIBullisharXiv β CS AI Β· Mar 96/10
π§ Researchers developed a new training method to improve the robustness of AI foundation models like SAM3 for medical image segmentation by reducing sensitivity to prompt variations. The approach groups semantically similar prompts together and uses consistency constraints to ensure more reliable predictions across different prompt formulations.
AIBullisharXiv β CS AI Β· Mar 96/10
π§ Researchers developed an interpretable AI framework for fetal ultrasound image classification that incorporates medical concepts and clinical knowledge. The system uses graph convolutional networks to establish relationships between key medical concepts, providing explanations that align with clinicians' cognitive processes rather than just pixel-level analysis.
AIBullisharXiv β CS AI Β· Mar 37/107
π§ Researchers have developed CT-Flow, an AI framework that mimics how radiologists actually work by using tools interactively to analyze 3D CT scans. The system achieved 41% better diagnostic accuracy than existing models and 95% success in autonomous tool use, potentially revolutionizing clinical radiology workflows.
AIBullisharXiv β CS AI Β· Mar 37/106
π§ Researchers developed M-Gaussian, a new AI framework that adapts 3D Gaussian Splatting for efficient multi-stack MRI reconstruction. The method achieves 40.31 dB PSNR while being 14 times faster than existing implicit neural representation methods, offering improved balance between quality and computational efficiency.
AIBullisharXiv β CS AI Β· Mar 36/1012
π§ Researchers developed FMCT/EFMCT, a new Flow Matching-based framework for CT medical imaging reconstruction that significantly improves computational efficiency over existing diffusion models. The method uses deterministic ordinary differential equations and velocity field reuse to reduce neural network evaluations while maintaining reconstruction quality.
AIBullisharXiv β CS AI Β· Mar 36/107
π§ Researchers developed a spatiotemporal diffusion autoencoder using CT brain images to predict stroke outcomes and evolution. The AI model achieved best-in-class performance for predicting next-day severity and functional outcomes using a dataset of 5,824 CT images from 3,573 patients across two medical centers.
AIBullisharXiv β CS AI Β· Mar 36/104
π§ Researchers developed MAP-Diff, a multi-anchor guided diffusion framework that improves 3D whole-body PET scan denoising by using intermediate-dose scans as trajectory anchors. The method achieves significant improvements in image quality metrics, increasing PSNR from 42.48 dB to 43.71 dB while reducing radiation exposure for patients.
AINeutralarXiv β CS AI Β· Mar 26/1017
π§ Researchers conducted a systematic benchmark study on multimodal fusion between Electronic Health Records (EHR) and chest X-rays for clinical decision support, revealing when and how combining data modalities improves healthcare AI performance. The study found that multimodal fusion helps when data is complete but benefits degrade under realistic missing data scenarios, and released an open-source benchmarking toolkit for reproducible evaluation.
AIBullisharXiv β CS AI Β· Mar 27/1016
π§ Researchers developed MINT, a framework that transfers knowledge from MRI brain scans to speech analysis for early Alzheimer's detection. The system achieves comparable performance to speech-only methods while being grounded in neuroimaging biomarkers, enabling population-scale screening without requiring expensive MRI scans at inference.
AIBullisharXiv β CS AI Β· Feb 276/105
π§ Researchers developed MedSegLatDiff, a new AI framework combining variational autoencoders with diffusion models for medical image segmentation. The system operates in compressed latent space to reduce computational costs while generating multiple plausible segmentation masks, achieving state-of-the-art performance on skin lesion, polyp, and lung nodule datasets.
AIBullishMicrosoft Research Blog Β· Jan 276/101
π§ Microsoft Research introduces UniRG, a new AI system that uses multimodal reinforcement learning to improve medical imaging report generation. The system addresses challenges with varying reporting schemes that current medical vision-language models struggle to handle effectively.
