Volume 7 Number 1 2023
AI Co-Pilot in Emergency Medicine: Transforming Triage, Diagnostics, and Workflow Efficiency for Smarter, Safer Patient Care – A Systematic Review
Maryam Abdulrahman Al-Mutairi 1*, Abdulaziz Saeed Alghamdi 1, Mohammed Ibraheem Alshalan 1, Hezam Motlaq H. Alsahly 1, Turki Ahmed k. Almatrafi 1, Fawaz Muslih Roud Alanazi 1, Mohammed Salem S Alqahtani 1, Al Shamari, Abdullah Mohammed K 1, Ali Ishq Mualla Almutairi 1, Mohammed Ali Ibrahim Bakri 1, Saif Mohammed Abdulmohsen Alsahman 1
Journal of Angiotherapy 7 (1) 1-9 https://doi.org/10.25163/angiotherapy.7110353
Submitted: 01 July 2023 Revised: 26 August 2023 Published: 28 August 2023
Abstract
Background: The challenges faced by emergency departments (EDs), including overcrowding, diagnostic errors, and operational inefficiencies, can adversely impact patient care and outcomes. The AI Co-Pilot model incorporates both diagnostic and operational artificial intelligence (AI) to improve clinical decision-making and workflows for all ED stakeholders. Aim: This review seeks to determine the current applications of AI in EDs, assessing relevant applications in triage, imaging, predictive diagnostics, and resource allocation, and to analyze the benefits and limitations of these AI applications, as well as how they can be implemented in the future. Methods: A systematic literature review was conducted using primary databases (PubMed, MEDLINE, Scopus, Google Scholar, and IEEE Xplore) to identify peer-reviewed studies published between 2015 and 2023. Keywords in the search strategy included "artificial intelligence," "emergency department," and "triage." Studies were included if they focused on AI applications in EDs and outcomes were reported. Results: AI has been shown to improve diagnostic accuracy (i.e., AUC 0.85-0.91), minimize triage wait times by 18.7 minutes, and improve allocative and documentation efficiency. Case studies highlight that AI-based CT evaluation of COVID-19 analysis and triage optimization reduced the radiologist workload and reduced diagnostic errors (i.e., by 16%). Major challenges to wider implementation were algorithmic bias, privacy and data security, and integration into clinical workflow. Conclusion: The AI Co-Pilot model offers a substantial improvement in ED workflow and patient care; however, its implementation requires an ethical framework for AI, interoperable approaches, and explainable AI before widespread adoption. Further prospective studies are warranted to confirm the findings and operationalization.
Keywords: Emergency department, Artificial intelligence, operational AI, diagnostic AI, triage.
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