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AI offers promise in the realm of chronic disease management, including diabetes, obesity, and PCOS, but must be approached with caution.
At the Mount Sinai Health System we have successfully translated many algorithms from concept to testing and clinical care, ...
Under the business model Fujitsu Uvance, Fujitsu is building an ecosystem of Uvance Partners that will apply data and AI towards the resolution of social issues. Through co-creation with these ...
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Asian News International on MSNIndia's Ayush innovations featured in WHO's landmark brief on AI in traditional medicineIn a significant milestone for global healthcare innovation, the World Health Organization (WHO) has released a technical ...
Introduction to Computer Vision in HealthcareLet’s face it—healthcare can be slow, expensive, and prone to human error. But what if a camera and some smart algorithms could help change that? That's ...
Longitudinal tracking of neuronal activity from the same cells in the developing brain using Track2p
This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides ...
Deep-learning ... algorithms will shift medical imaging from pattern recognition to quantitative, predictive precision.
The global Medical Imaging Market is valued at USD 41.62 Billion in 2024 and is projected to reach a value of USD 71.88 ...
Explore how AI-driven Smart Diagnostics are revolutionizing healthcare, as the market shifts from research to clinical practice. Discover the potential for premium diagnostics, pricing trends, and ...
Woese Institute for Genomic Biology are a step closer to realizing this goal by integrating machine learning-based analysis into point-of-care biosensing technologies. The new method, ...
More information: Yuqiao Yang et al, Patch-Based Deep-Learning Model With Limited Training Dataset for Liver Tumor Segmentation in Contrast-Enhanced Hepatic Computed Tomography, IEEE Access (2025).
The integration of deep learning in neuroimaging enhances diagnostic capabilities, offering new insights into neurological disorders and treatment responses.
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