#ct-scans News & Analysis

5 articles tagged with #ct-scans. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · Jun 237/10

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Human and AI collaboration for pulmonary nodule segmentation

Hi-Seg, a human-in-the-loop segmentation framework built on the Segment Anything Model, achieved 85% accuracy in pulmonary nodule detection across 1,179 patients, outperforming five state-of-the-art AI models by 10-22%. The research demonstrates that non-experts with brief training can match junior medical professionals' performance, suggesting foundation models can be safely integrated into clinical workflows while reducing annotator burden.

AIBullisharXiv – CS AI · Mar 57/10

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Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset

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 · Mar 37/107

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CT-Flow: Orchestrating CT Interpretation Workflow with Model Context Protocol Servers

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 – CS AI · Mar 36/107

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Stroke outcome and evolution prediction from CT brain using a spatiotemporal diffusion autoencoder

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.

AINeutralarXiv – CS AI · Mar 124/10

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Prompting with the human-touch: evaluating model-sensitivity of foundation models for musculoskeletal CT segmentation

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.