ArticleClinical and experimental emergency medicine2023
Explainable artificial intelligence in emergency medicine: an overview.
Article in Clinical and experimental emergency medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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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.
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Who cites it
35 citing papers in PubMed.
- Article
- The PALLI-AI Framework: An Evidence-Informed Checklist for Designing and Reporting Trustworthy Artificial Intelligence Tools in Palliative and End-of-Life Care.Healthcare (Basel, Switzerland) · 2026Article
- Temporal trends and patterns of pre-hospital emergency medical service utilization in Yasuj, Southwest Iran: a four-year registry-based cross-sectional study.International journal of emergency medicine · 2026Article
- Building a Global Research Network for Fair, Accountable, Interpretable, and Responsible AI in Emergency Care: Protocol for a FAIR-EC Study.JMIR research protocols · 2026Article
- The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis.Emergency medicine Australasia : EMA · 2026Article
- Quorum-sensing, microbiome interactions, and emerging artificial intelligence-assisted anti-virulence strategies in Salmonella Typhi: a critical review of translational opportunities and challenges.Archives of microbiology · 2026Review
- Machine Learning for Prediction of High-Risk Infections in Patients With Cancer.Cancer medicine · 2026Article
- Causal Responsibility Based Explainable AI for Vibrational Spectroscopy Applied to Oral FTIR and Oesophageal Raman Diagnostics.Research square · 2026Article
- Artificial Intelligence-Enabled ECG for Elevated E/e' on Echocardiography: Hemodynamic Relevance and Prognostic Value.Journal of the American Heart Association · 2026Article
- AI-assisted age estimation from occlusal tooth wear using biofluorescence imaging.Scientific reports · 2026Article
- Artificial intelligence-driven cluster analysis for identifying clinical phenotypes in suspected sepsis patients in the emergency department.BMC emergency medicine · 2026Article
- Advances in nanotechnology for the diagnosis and management of autoimmune diseases.Asian journal of pharmaceutical sciences · 2026Review
- Artificial intelligence in clinical physiology: System-wise applications in diagnostics, monitoring, and medical education.Journal of family medicine and primary care · 2026Article
- Automated chain-of-thought evaluation framework for large language model-generated emergency department documentation: a simulation-based study.Clinical and experimental emergency medicine · 2026Article
- Artificial intelligence in emergency medicine critical care.Clinical and experimental emergency medicine · 2026Article
- How do dialysis nurses and AI reason clinically? A scenario-based comparative study.BMC nursing · 2026Article
- Clinical Effectiveness of an Artificial Intelligence-Based Prediction Model for Cardiac Arrest in General Ward-Admitted Patients: A Non-Randomized Controlled Trial.Diagnostics (Basel, Switzerland) · 2026Article
- Research trends and ethical perspectives on explainable artificial intelligence in emergency medicine: a bibliometric analysis.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Review
- Audit-as-code: a policy-as-code framework for continuous AI assurance.Frontiers in artificial intelligence · 2026Article
- Neural network for natural language processing to determine treatment urgency in an ophthalmology emergency department.The British journal of ophthalmology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Artificial intelligence (AI) and machine learning (ML) have potential to revolutionize emergency medical care by enhancing triage systems, improving diagnostic accuracy, refining prognostication, and optimizing various aspects of clinical care. However, as clinicians often lack AI expertise, they might perceive AI as a "black box," leading to trust issues. To address this, "explainable AI," which teaches AI functionalities to end-users, is important. This review presents the definitions, importance, and role of explainable AI, as well as potential challenges in emergency medicine. First, we introduce the terms explainability, interpretability, and transparency of AI models. These terms sound similar but have different roles in discussion of AI. Second, we indicate that explainable AI is required in clinical settings for reasons of justification, control, improvement, and discovery and provide examples. Third, we describe three major categories of explainability: pre-modeling explainability, interpretable models, and post-modeling explainability and present examples (especially for post-modeling explainability), such as visualization, simplification, text justification, and feature relevance. Last, we show the challenges of implementing AI and ML models in clinical settings and highlight the importance of collaboration between clinicians, developers, and researchers. This paper summarizes the concept of "explainable AI" for emergency medicine clinicians. This review may help clinicians understand explainable AI in emergency contexts.
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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.