This scoping review aims to identify regulator-approved ophthalmic image analysis artificial intelligence as a medical device (AIaMD) in three jurisdictions, examine their characteristics and regulatory approvals, and evaluate the available evidence underpinning them, as…
Continue Reading
Artificial intelligence (AI)-ECG-derived age (AI-ECG age) and Heart Delta Age (HDA)—the difference between AI-ECG and chronological age—are emerging tools for assessing cardiovascular health. We systematically searched PubMed, Embase, Web of Science, and Scopus from…
Continue Reading
Suicide rates are rising in the U.S., prompting interest in contributing factors like smartphone use. While screen time has been linked to mental health, its role in suicide risk is unclear. This study examined…
Continue Reading
Artificial intelligence (AI) models are promising tools for predicting antimicrobial susceptibility in gram-negative bloodstream infections (GN-BSI). Single-center study on hospitalized patients with GN-BSI, over 7-year period, aimed to predict resistance to fluoroquinolones (FQ-R), third…
Continue Reading
To assess if remote patient monitoring (RPM) programs for chronic conditions are positioned to advance equity through inclusive practices, 119 papers were evaluated against 11 multi-level equity parameters. Equity parameters were inconsistent and incompletely…
Continue Reading
Ultrasound assessment of fetal size and growth is the mainstay of monitoring fetal well-being during pregnancy, as being small for gestational age (SGA) or large for gestational age (LGA) poses significant risks for both…
Continue Reading
Millions of shift workers in the U.S. face an increased risk of depression, cancer, and metabolic disease, yet individual responses to shift work vary widely. We find that a conserved biological system of morning…
Continue Reading
Wearable medical devices are becoming increasingly common in everyday life, and thus is the reliance on them, the data they generate, and the resulting treatment plans. However, recent events in the supply chains of…
Continue Reading
Federated learning (FL) is advancing cancer research by enabling privacy-preserving collaborative training of machine learning (ML) models on diverse, multi-centre data. This systematic review synthesises current knowledge on state-of-the-art FL in oncology, focusing on…
Continue Reading
