13 articles tagged with #radiology. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
AINeutralarXiv – CS AI · Mar 97/10
🧠Researchers evaluated 34 large language models on radiology questions, finding that agentic retrieval-augmented reasoning systems improve consensus and reliability across different AI models. The study shows these systems reduce decision variability between models and increase robust correctness, though 72% of incorrect outputs still carried moderate to high clinical severity.
AIBullisharXiv – CS AI · Mar 57/10
🧠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.
AIBullisharXiv – CS AI · 1d ago6/10
🧠Researchers propose INFORM-CT, an AI framework combining large language models and vision-language models to automate detection and reporting of incidental findings in abdominal CT scans. The system uses a planner-executor approach that outperforms traditional manual inspection and existing pure vision-based models in accuracy and efficiency.
AIBullisharXiv – CS AI · Apr 76/10
🧠Researchers introduced VERT, a new LLM-based metric for evaluating radiology reports that shows up to 11.7% better correlation with radiologist judgments compared to existing methods. The study demonstrates that fine-tuned smaller models can achieve significant performance gains while reducing inference time by up to 37.2 times.
AIBullisharXiv – CS AI · Mar 276/10
🧠DeepFAN, a transformer-based AI model, achieved 93.9% diagnostic accuracy for lung nodule classification and significantly improved junior radiologists' performance by 10.9% in clinical trials. The model was trained on over 10,000 pathology-confirmed nodules and validated across 400 cases at three medical institutions.
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AIBullisharXiv – CS AI · Mar 176/10
🧠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 – CS AI · Mar 36/106
🧠Researchers created OpenRad, a curated repository containing approximately 1,700 open-access AI models for radiology. The platform aggregates scattered radiology AI research into a standardized, searchable database that includes model weights, interactive applications, and spans all imaging modalities and radiology subspecialties.
AIBullisharXiv – CS AI · Mar 37/107
🧠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.
AINeutralarXiv – CS AI · Mar 37/107
🧠Researchers identify a critical flaw in Vision-Language Model evaluation for radiology, where high benchmark scores mask models' failure to generate clinically specific terminology. They propose new metrics including Clinical Association Displacement (CAD) to measure bias and clinical signal loss across demographic groups.
AIBullisharXiv – CS AI · Mar 36/104
🧠Researchers introduce BoxMed-RL, a new AI framework that uses chain-of-thought reasoning and reinforcement learning to generate spatially verifiable radiology reports. The system mimics radiologist workflows by linking visual findings to precise anatomical locations, achieving 7% improvement over existing methods in key performance metrics.
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AIBullisharXiv – CS AI · Mar 27/1016
🧠Researchers developed a neurosymbolic verification framework to audit logical consistency in AI-generated radiology reports, addressing issues where vision-language models produce diagnostic conclusions unsupported by their findings. The system uses formal verification methods to identify hallucinations and missing logical conclusions in medical AI outputs, improving diagnostic accuracy.
AIBullisharXiv – CS AI · Mar 26/1012
🧠Researchers have developed Radiologist Copilot, an AI agentic framework that orchestrates specialized tools to complete the entire radiology reporting workflow beyond simple report generation. The system integrates image localization, interpretation, template selection, report composition, and quality control to support radiologists throughout the comprehensive reporting process.
AIBullisharXiv – CS AI · Mar 24/105
🧠Researchers have developed R2GenCSR, a new AI framework for generating radiology reports that uses Mamba architecture instead of Transformers to reduce computational complexity while maintaining performance. The system leverages context retrieval and large language models to produce high-quality medical reports from X-ray images.
