BACKGROUND: Preventive strategies integrated with digital health and artificial intelligence (AI) have significant potential to mitigate the global burden of atherosclerotic cardiovascular disease (ASCVD). AI-enabled clinical decision support (CDS) s...
Journal of computer-aided molecular design
Nov 25, 2025
Antimicrobial Resistance (AMR) is a global concern demanding high-throughput and precise AMR surveillance strategies. This review provides a comprehensive list of Artificial Intelligence (AI) driven frameworks widely employed in the early detection, ...
Journal of computer-aided molecular design
Nov 25, 2025
Early Breast Cancer (BC) Diagnosis has the potential to cut BC death rates in the long term drastically. Identifying early-stage cancer cells is the most crucial step in determining the best prognosis. Despite recent advances in the use of AI-based m...
Early prediction of hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is critical for improving clinical outcome and guiding timely interventions. This study focuses on assessing the effectiveness of artificial intelligence (AI)...
Oncology clinical trials play a pivotal role in the development of new therapeutic options; however, their implementation remains an extremely costly and time-consuming process. Artificial intelligence can open new horizons in the design and conduct ...
International journal of health geographics
Nov 22, 2025
BACKGROUND: Mental-health geomatics require reliable ways to convert high-frequency GPS trajectories into meaningful place types that support indicators such as homestay, location entropy, and spatial extent of daily activities. Raw coordinates are t...
While artificial intelligence (AI) has demonstrated potential in automating clinical trial matching, most existing solutions rely on high-level structured data or oversimplified criteria. This study introduces a framework to structure and analyze eli...
PURPOSE: This study aims to evaluate the diagnostic performance of PET-based artificial intelligence (AI) for differentiating Parkinson's disease (PD) from normal controls (NC) or atypical parkinsonism (AP).
OBJECTIVE: To evaluate the effectiveness of a deep learning-based style adaptation strategy in improving the diagnostic accuracy and cross-camera generalisability of artificial intelligence (AI) for detecting diabetic retinopathy (DR).
OBJECTIVES: Postoperative complications (PCs) require substantial resources to manage and are cumbersome to monitor. Artificial intelligence (AI), particularly natural language processing (NLP), offers a potential solution by automating and streamlin...
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