#clinical-ai News & Analysis

131 articles tagged with #clinical-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

131 articles

AINeutralarXiv – CS AI · Mar 95/10

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Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness

This academic review examines the integration of foundation models and AI agents in computational pathology for medical applications. While AI shows promising performance in diagnosis and treatment prediction tasks, real-world clinical adoption remains limited due to economic, technical, and regulatory challenges.

AINeutralarXiv – CS AI · Mar 54/10

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Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast

Researchers developed a framework to analyze how demographic attributes (age, sex, race) can be predicted from brain MRI scans by separating anatomical structure from acquisition-dependent contrast differences. The study found that demographic predictability primarily stems from anatomical variation rather than imaging artifacts, suggesting bias mitigation in medical AI must address both sources.

AINeutralarXiv – CS AI · Mar 44/102

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CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram

Researchers developed CASR-Net, a deep learning pipeline for automated coronary artery segmentation in X-ray angiograms that combines image preprocessing, UNet-based segmentation, and refinement stages. The system achieved superior performance with 61.43% IoU and 76.10% DSC on public datasets, potentially improving clinical diagnosis of coronary artery disease.

AINeutralarXiv – CS AI · Mar 35/108

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How Well Do Multimodal Models Reason on ECG Signals?

Researchers introduce a new framework for evaluating how well multimodal AI models reason about ECG signals by breaking down reasoning into perception (pattern identification) and deduction (logical application of medical knowledge). The framework uses automated code generation to verify temporal patterns and compares model logic against established clinical criteria databases.

AIBullisharXiv – CS AI · Mar 35/104

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Noise reduction in BERT NER models for clinical entity extraction

Researchers developed a Noise Removal model to improve precision in clinical entity extraction using BERT-based Named Entity Recognition systems. The model uses advanced features like Probability Density Maps to identify weak vs strong predictions, reducing false positives by 50-90% in clinical NER applications.

AIBullishOpenAI News · Dec 144/106

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Increasing accuracy of pediatric visit notes

Summer Health has partnered with OpenAI to enhance pediatric healthcare by improving the accuracy of doctor's visit notes. This collaboration aims to reimagine how pediatric medical documentation is handled through AI technology.

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