ReviewAcademic emergency medicine : official journal of the Society for Academic Emergency Medicine2025
Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.
Review in Academic emergency medicine : official journal of the Society for Academic Emergency Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07632859 (Diagnostic Accuracy of Two Large Language Models Against a Blinded Specialist Consensus Standard in Turkish Emergency Department Notes), which is not on this map. Cited by 27 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.
Diagnostic Accuracy of Two Large Language Models Against a Blinded Specialist Consensus Standard in Turkish Emergency Department Notes: A Retrospective Study of 600 Cases
Who cites it
27 citing papers in PubMed.
- Implementation of Expanding Primary Care Teams to Improve Anemia Diagnosis.Journal of primary care & community healthTrial
- Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce.Journal of the American College of Emergency Physicians open · 2026Article
- An Evidence-Based Framework for Patient-Facing Artificial Intelligence Integration in the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Transforming Human-AI Collaboration in Emergency Care: The Role of AI in Adaptive Support Across Diagnostic Reasoning Modes.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2026Article
- Diagnostic capability of large language models in critically ill patients: a prospective single-centre study comparing ChatGPT, Claude, and Gemini with emergency physicians.BMC emergency medicine · 2026Observational
- Operationalising adult eating disorders admission criteria in Australian emergency departments: a clinical decision-support approach.Journal of eating disorders · 2026Article
- Awareness, Educational Needs, and Curriculum Preferences Regarding AI and Medical Big Data Education Among Clinical Medicine Undergraduates: Cross-Sectional Survey Study.JMIR formative research · 2026Article
- Integrating mission-aligned value with cost to assess the economic impact of AI in healthcare.NPJ digital medicine · 2026Review
- Screening for Missed Opportunities for Diagnosis in the ED Using eTriggers and Large Language Models.JAMA network open · 2026Article
- Ethical AI in healthcare: insights from European policy discourses.BMC medical ethics · 2026Article
- The Role of Artificial Intelligence in Shaping the Doctor-Patient Relationship: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Article
- Large Language Models Evaluation of Medical Licensing Examination Using GPT-4.0, ERNIE Bot 4.0, and GPT-4o.Bioengineering (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
- Benchmarking Generative AI Tools for Interpretation of the WHO TB Mutation Catalogue.BMC digital health · 2026Article
- Operationalising Inclusion for Participatory Design: Worked Examples for TRIPOD+AI & PROBAST+AI.Health services insights · 2026Article
- AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.Journal of nanobiotechnology · 2025Review
- The Potential and Peril of Artificial Intelligence in the Emergency Department.Journal of medical Internet research · 2025Article
- Neural network for natural language processing to determine treatment urgency in an ophthalmology emergency department.The British journal of ophthalmology · 2025Article
- Exploring the dark side of the moon: diagnostic errors in critically ill patients.Intensive care medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must make rapid decisions with limited information, often under cognitive overload. Artificial intelligence (AI) offers promising solutions to improve diagnostic errors in three key areas: information gathering, clinical decision support (CDS), and feedback through quality improvement. AI can streamline the information-gathering process by automating data retrieval, reducing cognitive load, and providing clinicians with essential patient details quickly. AI-driven CDS systems enhance diagnostic decision making by offering real-time insights, reducing cognitive biases, and prioritizing differential diagnoses. Furthermore, AI-powered feedback loops can facilitate continuous learning and refinement of diagnostic processes by providing targeted education and outcome feedback to clinicians. By integrating AI into these areas, the potential for reducing diagnostic errors and improving patient safety in the ED is substantial. However, successfully implementing AI in the ED is challenging and complex. Developing, validating, and implementing AI as a safe, human-centered ED tool requires thoughtful design and meticulous attention to ethical and practical considerations. Clinicians and patients must be integrated as key stakeholders across these processes. Ultimately, AI should be seen as a tool that assists clinicians by supporting better, faster decisions and thus enhances patient outcomes.
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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.