Latest AI and machine learning research in surveillance for healthcare professionals.
OBJECTIVE: This study aimed to develop and validate machine learning (ML) models for predicting the prognosis of status epilepticus (SE) patients with multisystem complications. METHODS: We developed predictive models using six ML algorithms: least absolute shrinkage and selection operator (LASSO) logistic regression, k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), ran...
BACKGROUND: Artificial intelligence (AI) models are increasingly being used in medical education. Although models like ChatGPT have previously demonstrated strong performance on United States Medical Licensing Examination (USMLE)-style questions, newer AI tools with enhanced capabilities are now available, necessitating comparative evaluations of their accuracy and reliability across different med...
BACKGROUND: Mechanical thrombectomy (MT) is the standard treatment for acute posterior circulation artery occlusion (PCAO), but predicting outcomes re...
BACKGROUND: Telepathology has emerged as a transformative digital health solution to address the global shortage of pathologists and the unequal distr...
BACKGROUND: Digital biomarkers are gaining interest as proxy markers for mental health, as they enable passive and continuous data collection. However...
Cardiovascular disease (CVD) remains the leading cause of death among women globally, with significant mortality and poorer outcomes compared with men...
OBJECTIVES: This scoping review aims to assess the role of machine learning in workplace mental health research by systematically analyzing existing s...
BACKGROUND: There is an urgent need to evaluate the efficacy of novel therapeutics that have been approved for use in adults with IgA nephropathy (IgA...
This study addresses the difficulty of predicting triboelectric nanogenerator outputs across heterogeneous devices and reporting conventions. A dual-t...
PURPOSE: To map and synthesise current evidence on machine learning (ML) applications for anterior cruciate ligament (ACL) injury risk estimation, reh...
OBJECTIVE: To examine how continually updated, living evidence and gap maps (L-EGMs) with an online presence report planned update schedules, retireme...
BACKGROUND: Artificial intelligence (AI) is increasingly embedded in radiology research and practice, yet concerns about the reproducibility of AI stu...
The interaction between autophagy and ferroptosis has resulted in the identification of novel approaches for the treatment of lung cancer (LC). The tw...
OBJECTIVES: This scoping review examined the current application of artificial intelligence (AI)/machine learning (ML) models on social media platform...
Chronic kidney disease (CKD) represents a heavy global health burden associated with increased mortality and morbidity and high economic impact. Chron...
Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a significant complication in surgical patients. Ar...
PURPOSE: Although evidence-based practice (EBP) promotes better clinical practice, implementing it in speech-language pathology is challenging. A limi...
Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning (ML) systems into high-stakes domains such as digit...
AIM: This study aimed to evaluate the ability of three generative artificial intelligence tools (ChatGPT, Gemini and DeepSeek) to generate clinically ...
Brucellosis is an important zoonotic disease affecting humans, livestock, and wildlife, yet prevalence estimates in wild species are often underestima...