AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
It is suggested that sustained clinician engagement, rather than algorithmic accuracy, may be the key barrier to effective clinical AI use in emergency departments.
Background: Emergency Department (ED) return visits are commonly reviewed for quality assurance, but are often limited (e.g., to revisits within 48-72 hours) to increase actionable finding yield while minimizing chart review burden. Those limitations may lead to missed quality improvement opportunities. Methods: We con...
Emergency departments (EDs) operate under time pressure, diagnostic uncertainty, and cognitive overload. Artificial intelligence (AI)–driven clinical decision support systems (CDSS) promise to enhance diagnostic accuracy, risk stratification, and workflow efficiency. However, translation from algorithmic performance to...
Sustainable implementation of AI-CDSSs in emergency medicine will require prospective multi-site evaluation, sociotechnical integration, adaptive governance, and greater attention to equity.
Although promising, LLM-based systems are not yet reliable enough for autonomous medical diagnosis, and multiple recommendations for future research are contained to ensure a high level of safety, transparency, and clinical applicability for LLMs and other AI/ML-related technologies and devices.
Objectives To evaluate whether access to a certified large language model (LLM)-based clinical decision support system improves physician diagnostic performance in rheumatology compared with conventional diagnostic resources alone. Methods In this multicentre, open-label, randomised controlled trial, 82 physicians from...
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