BMC medical informatics and decision making
Jan 17, 2025
BACKGROUND: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographi...
Gastrointestinal endoscopy clinics of North America
Jan 16, 2025
Integration of artificial intelligence (AI) into medical devices and services promises significant improvements in the diagnosis and treatment of disease. This article reviews current payment pathways for AI medical technology and the regulatory issu...
American journal of veterinary research
Jan 16, 2025
The US FDA's Center for Veterinary Medicine (CVM) is advancing its leadership in veterinary science by integrating AI and machine learning (ML) into its regulatory framework and scientific initiatives. This paper explores the CVM's strategic approach...
BACKGROUND: Breast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and survival rates.
BACKGROUND AND AIM: Changes in cognitive function are commonly associated with aging in patients with cardiovascular diseases. The objective of this research was to construct and validate a nomogram-based predictive model for the identification of co...
The challenge of reconstructing air temperature for environmental applications is to accurately estimate past exposures even where monitoring is sparse. We present XGBoost-IDW Synthesis for air temperature (XIS-Temperature), a high-resolution machine...
Journal of the American College of Radiology : JACR
Jan 10, 2025
PURPOSE: Surveys to assess views about artificial intelligence (AI) of various diagnostic radiology constituencies have revealed interesting combinations of enthusiasm, caution, and implementation priorities. We surveyed academic radiology leaders ab...
PURPOSE: The objective of this study is to investigate the predictive ability of machine learning models for imbalanced outcomes from national survey data without the use of sampling weights.
Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine...
Self-rated health (SRH) is a significant predictor of future health outcomes. Despite the contribution of psychological factors in individuals' subjective health assessments, prior studies of machine learning-based prediction models primarily focused...
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