Latest AI and machine learning research in patient safety / risk management for healthcare professionals.
OBJECTIVES: Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can have detrimental effects, prediction of the need for ECT could improve outcomes via more timely treatment initiation. Therefore, this study aimed to predict the need for ECT following admission to a psychiatric hospital. METHODS: This study was base...
BACKGROUND: There is increasing research on machine learning in predicting venous thromboembolism after joint arthroplasty, but the quality and clinical applicability of these models remain uncertain. OBJECTIVE: This systematic review aims to evaluate the predictive performance and methodological quality of machine learning models for venous thromboembolism risk after joint replacement surgery. ME...
The use of machine learning (ML) models in forensic anthropology (FA) has increased in the last half decade; however, there is a lack of a standardize...
Data-driven decision-making (DDDM) has become integral to managerial and organizational processes in the era of digitalization and internationalizatio...
BACKGROUND: Since November 2022, conversational tools powered by generative artificial intelligence (GAI) have become integrated into academic and pro...
OBJECTIVE: To evaluate pixel-based measurements of the minimum nasal and temporal extent of retinal vascularization (NERV and TERV) from RetCam images...
BACKGROUND: Sexual health concerns following prostate cancer treatment are common yet often insufficiently addressed in clinical practice, particularl...
A COMMENTARY ON: Ziaei, S., Samani, D., Behjati, M. et al. Accuracy of artificial intelligence in orthodontic extraction treatment planning: a systema...
BACKGROUND: Early identification of disability risk in community-dwelling older adults has emerged as a critical public health priority. An increasing...
PURPOSE: To identify factors associated with accelerated retinal aging based on machine learning predictions of age using fundus images from teleretin...
PURPOSE: Accurately measuring geographic atrophy (GA) progression in clinical trials is challenging, owing to its slow and variable nature. This study...
AIM: To synthesize literature on algorithmic bias and transparency in artificial intelligence tools used in nursing education and identify common appl...
OBJECTIVES: To assess healthcare professionals' attitudes toward artificial intelligence (AI) in healthcare and to identify personal and professional ...
PURPOSE: To evaluate the capability of large language models (LLM), specifically GPT-4 and o1, in assessing adherence to the MI-CLEAR-LLM checklist in...
BACKGROUND: Recent advances have highlighted the potential of artificial intelligence (AI) systems to assist clinicians with administrative and clinic...
BACKGROUND: Social media is a significant source of information for post-secondary students, who are usually at the age at which many common mental di...
PURPOSE: To leverage artificial intelligence-based OCT analysis to classify age-related macular degeneration (AMD) images into distinct subgroups base...
BACKGROUND: Reporting of COVID-19 prognostic models frequently falls short of established standards. The TRIPOD checklist and its 2024 AI extension (T...
BACKGROUND: This systematic review compiles evidence and examines how various artificial intelligence (AI) approaches, including machine learning (ML)...
PURPOSE: To develop a multimodal, multitask artificial intelligence (AI) model to classify postfitting corneal topography patterns and predict axial l...