Latest AI and machine learning research in product alert for healthcare professionals.
BACKGROUND: Artificial intelligence and machine learning (AI/ML) may strengthen hospital infection prevention and control (IPC) through automated surveillance, early warning, and decision support, but the evidence base is fragmented and often limited to retrospective model development. METHODS: We conducted a PRISMA 2020 systematic review to synthesize studies of AI/ML in acute-care hospital IPC, ...
In 2022, Step 1 of the United States Medical Licensing Examination transitioned to pass/fail scoring, removing a major performance-oriented incentive that historically shaped how and why students prepared for the exam. While Step 1 is typically taken before clerkships, some medical schools have shifted the exam to after core clerkships, citing potential benefits for learning and integration. This ...
Accurate prediction of liquefaction-induced lateral displacement is essential for seismic risk assessment, resilient infrastructure design, and cost-e...
Soil microbiomes are increasingly recognized as valuable indicators in forensic investigations, but microbial dynamics in mass graves remain poorly un...
Fecal microbiota transplantation (FMT) has emerged as a promising therapy for gastrointestinal diseases, yet its clinical efficacy remains individuall...
BACKGROUND: Patients' digital access to their personal health data is becoming increasingly common worldwide. However, medical documentation often con...
PURPOSE: To evaluate the incidence of uterine tachysystole and determine if nurses' management of tachysystole using an artificial intelligence-enable...
PURPOSE: Non-mass enhancement (NME) in breast magnetic resonance imaging (MRI) is a diagnostically challenging entity due to overlapping benign and ma...
INTRODUCTION: Intravenous thrombolysis (IVT) with tissue-type plasminogen activator (tPA) is a cornerstone of acute ischemic stroke treatment, yet its...
PURPOSE: Mechanical thrombectomy (MT) improves stroke outcomes, but is limited by a lack of local treatment access. Widespread distribution of reinfor...
This study used machine learning to objectively identify seizures in the electroencephalogram of a model of post-traumatic epilepsy based on fluid per...
OBJECTIVE: Phase II of MVP-CHAMPION, a federal collaboration between the Veterans Affairs Healthcare System (VA) and the Department of Energy (DoE), l...
OBJECTIVE: Traditional readmission risk models relying on static discharge data have limited predictive performance and fail to capture patients' reco...
PURPOSE: Machine learning segmentation has emerged in tumor assessment with high performance in volumetric evaluation of brain tumors. It is unclear, ...
Human-elephant conflict (HEC) is emerging as a growing challenge to biodiversity conservation and rural livelihoods in Asia. Assam's Brahmaputra Flood...
Chronic hepatitis B remains a major global health challenge despite the advances in antiviral therapy. Although hepatitis B surface antigen (HBsAg) se...
BACKGROUND: Highly accessible and scalable, digital mental health interventions can reduce barriers associated with traditional treatment. Woebot for ...
For pharmacovigilance, the Pharmaceuticals and Medical Devices Agency in Japan has utilized real world data (RWD) from multiple sources, including ind...
Biomedical knowledge discovery increasingly relies on computational tools to uncover patterns in complex datasets, yet generating explainable, evidenc...
PURPOSE: Many potential Donation After Circulatory Death (DCD) donors do not progress to circulatory death within meaningful timeframes, leading to wa...