Dr. Michael Spaeder of the University of Virginia previews his upcoming HIMSS26 talk on using AI and machine learning to detect potentially catastrophic health events.
Meta wants us to believe there’s a difference between addiction and ‘problematic use.’ The harm to kids suggests otherwise ...
Optokinetic Nystagmus (OKN) is a natural reflexive eye movement in oculomotor studies, reflecting the health status of the ...
Machine learning algorithms that output human-readable equations and design rules are transforming how electrocatalysts for ...
Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the ...
Non-terrestrial networks have their own challenges that cellular networks didn't have. Will AI help solve them dynamically?
The small and complicated features of TSVs give rise to different defect types. Defects can form during any of the TSV ...
Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced ...
Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...
Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Individual prediction uncertainty is a key aspect of clinical prediction model performance; however ...
In the UK, there was a case where TGN1412, an immunotherapy under development, triggered a cytokine storm within hours of administration to humans, leading to multiple organ failure. Another example, ...
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