Latest AI and machine learning research in risk management for healthcare professionals.
BACKGROUND: Artificial intelligence (AI) systems are increasingly embedded in clinical diagnostics and referral pathways, yet when these tools contribute to patient harm, traditional medico-legal doctrines are strained by algorithmic opacity, automation bias, and distributed decision-making. Beyond legal uncertainty, AI integration raises fundamental ethical concerns regarding clinician moral agen...
OBJECTIVE: Antimicrobial Stewardship Programs (ASPs) need healthcare economic analyses to support and inform ASP strategies. This work aimed to determine whether widely available artificial intelligence (AI) platforms like Microsoft CopilotTM could facilitate healthcare economics analyses for ASP programs without dedicated healthcare economic supports. DESIGN: AI (Microsoft CopilotTM) was prompted...
Projectional radiography is vulnerable to artefacts that can impair image quality and obscure or mimic pathology, confounding image interpretation. Th...
INTRODUCTION: Chronic musculoskeletal pain (CMP) is a leading cause of incurred personal healthcare costs and disability in the USA. It disproportiona...
BACKGROUND: Artificial intelligence (AI) is increasingly utilized in surgical care for decision support, operative planning, intraoperative guidance, ...
Cultural and intangible heritage has been part of human daily life since time immemorial, fulfils a function within the community and acts as an eleme...
PURPOSE: AI governance commonly emphasises procurement, validation, deployment and performance monitoring but lack guidance on how embedded AI tools s...
BACKGROUND: Large language models (LLMs) are increasingly integrated into healthcare applications, but their tendency to generate hallucinations-factu...
BACKGROUND: Long-term androgen deprivation therapy (LT-ADT) with radiotherapy is standard-of-care for high-risk localized prostate cancer, with abirat...
Cardiovascular magnetic resonance (CMR) has emerged as the reference noninvasive modality for a comprehensive assessment of myocardial injury followin...
Artificial intelligence (AI) tools are shifting from passive, user-initiated tools to proactive agentic AI systems that are capable of autonomous, mul...
OBJECTIVE: The purpose of this study was to describe and synthesize the existing literature regarding the application of artificial intelligence (AI) ...
Accurate timekeeping is foundational to modern society, supporting critical technologies such as global navigation satellite systems (GNSS), high-freq...
BACKGROUND: This study aims to evaluate the effectiveness of deep learning algorithms in simulating standard acquisition time images from shortened ac...
BACKGROUND: While generative artificial intelligence (AI) is rapidly proliferating in healthcare research and clinical settings, there is a lack of ac...
BACKGROUND: Breast cancer (BC) is the most common cancer in women and the leading cause of cancer-related death worldwide. Systemic immune dysregulati...
Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 has substantially advanced the standardization of prostate MRI acquisition, interpret...
Artificial intelligence (AI), most often in the form of machine learning (ML), attracts high expectations across medicine and is often discussed as a ...
Artificial intelligence (AI) increasingly influences clinical decision-making, yet its recommendations may diverge from standard care. Although malpra...
AIM: The traditional three-level H&E sectioning protocol for prostate biopsies was developed for ultrasound-guided systematic sampling and predates le...