SynthesisBMC health services research2022
Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review.
Synthesis in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.
- The global effect of digital health technologies on health workers' competencies and health workplace: an umbrella review of systematic reviews and lexical-based and sentence-based meta-analysis.The Lancet. Digital health · 2023Pooled it
- Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce.Journal of the American College of Emergency Physicians open · 2026Article
- Beyond awareness: Defining AI readiness in Emergency Medicine across health systems.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026Article
- Real-World Implementation and Evaluation of AI-Driven Clinical Decision Support in Emergency Medicine: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Perception and challenges of artificial intelligence (AI) in Emergency Medicine: A multi-country study in Sub-Saharan Africa.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026Article
- Shadow AI in Swedish Health Care: Qualitative Analysis of Physicians' Free-Text Answers.Journal of medical Internet research · 2026Article
- Struggling to integrate artificial intelligence in prehospital emergency care in a developing country: exploration of the Iranian experts' views based on qualitative content analysis.International journal of emergency medicine · 2026Article
- Crowding in real-time: an artificial intelligence-based model to manage crowding in the emergency department (CRAMMED Study).BMC emergency medicine · 2026Article
- Artificial intelligence for predicting hospital admissions from the emergency department: a prospective, quasi-experimental study.Nature communications · 2026Article
- Artificial intelligence in trauma care: applications, ethical challenges, and pathways toward responsible integration.Current opinion in anaesthesiology · 2026Article
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- Impact, use, and implications of artificial intelligence in public health decision making by elected officials: a scoping review.Frontiers in public health · 2026Article
- Review
- Integrating AI-based triage in primary care: a qualitative study of Swedish healthcare professionals' experiences applying normalization process theory.BMC primary care · 2025Article
- Role of artificial intelligence in virtual emergency care: a protocol for a systematic review.BMJ open · 2025Article
- Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs.Clinical implant dentistry and related research · 2025Article
- Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.BMC emergency medicine · 2025Article
- Feasibility of a multimodal AI-based clinical assessment platform in emergency care: an exploratory pilot study.Frontiers in digital health · 2025Article
- Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.The journal of headache and pain · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis systematic literature review aims to demonstrate how Artificial Intelligence (AI) is currently used in emergency departments (ED) and how it alters the work design of ED clinicians. AI is still new and unknown to many healthcare professionals in emergency care, leading to unfamiliarity with its capabilities.
methodVarious criteria were used to establish the suitability of the articles to answer the research question. This study was based on 34 selected peer-reviewed papers on the use of Artificial Intelligence (AI) in the Emergency Department (ED), published in the last five years. Drawing on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, all articles were scanned, read full-text, and analyzed afterward.
resultsThe majority of the AI applications consisted of AI-based tools to aid with clinical decisions and to relieve overcrowded EDs of their burden. AI support was mostly offered during triage, the moment that sets the patient trajectory. There is ample evidence that AI-based applications could improve the clinical decision-making process.
conclusionThe use of AI in EDs is still in its nascent stages. Many studies focus on the question of whether AI has clinical utility, such as decision support, improving resource allocation, reducing diagnostic errors, and promoting proactivity. Some studies suggest that AI-based tools essentially have the ability to outperform human skills. However, it is evident from the literature that current technology does not have the aims or power to do so. Nevertheless, AI-based tools can impact clinician work design in the ED by providing support with clinical decisions, which could ultimately help alleviate a portion of the increasing clinical burden.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.