Abstract: Quantum machine learning (QML) presents a promising avenue for addressing complex classification challenges, yet its application in medical imaging remains largely unexplored. This work ...
Passive sensing via wearable devices and smartphones, combined with machine learning (ML), enables objective, continuous, and noninvasive mental health monitoring. Objective: This study aimed to ...
Background While the incidence of hospital adverse events appeared to be declining before 2019, the COVID-19 pandemic may ...
When a crime occurs in private, with no witnesses, a court contest is a tussle in which two stories compete to offer the most plausible explanation of the same facts. Photographs and audio recordings ...
Triage, Critical Clinical Workflow Process, Chest Pain, Electrocardiogram (EKG), Door-to-EKG (DTE) Time Share and Cite: ...
Background Autism spectrum disorder (ASD) is a neurodevelopmental condition characterised by impairments in social ...
Abstract: Integrating advanced technologies has revolutionized dental practice, transforming patient diagnosis and treatment. Radiographic examinations remain one of the most essential tools for ...
Background: The diagnosis of occupational pneumoconiosis requires more accurate predictive models. The purpose of this study is to screen blood markers associated with early pneumoconiosis development ...
Twenty-five years of research into complex systems shows why artificial intelligence will always produce errors in healthcare decisions, regardless of technological improvements or funding.
Objective: This study aims to identify the key risk factors for occupational exposure among oral healthcare workers and develop a predictive model using machine learning algorithms to lay the ...
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