4 articles tagged with #ct-scans. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
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.
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.
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.
Researchers evaluated 11 promptable foundation models for medical CT image segmentation across bone and implant identification tasks. The study found significant performance variations between models and strategies, with all models showing sensitivity to human prompt variations, suggesting current benchmarks may overestimate real-world performance.
