Systematic Literature Review: The Role of Artificial Intelligence in Emergency Department Decision Making

Background: Emergency Departments (EDs) face increasing pressure to make swift and accurate decisions. Artificial Intelligence (AI) has emerged as a potential solution to enhance clinical decision-making in this setting. Objectives: To evaluate the effectiveness of AI applications in diagnostic decision-making, triage processes, and outcome prediction in EDs, and to assess associated ethical concerns and patient safety considerations. Method: The search included PubMed, Embase, Web of Science, and Cochrane Library from inception to September 2023. Randomized controlled trials, cohort studies, and cross-sectional studies evaluating AI applications in ED settings were included. Studies were screened, data were extracted, and risk of bias was assessed using the quality assessment of diagnostic accuracy studies (QUADAS-2) tool and Cochrane risk of bias tool. Results: Of 1,245 records identified, 29 studies met inclusion criteria. AI models demonstrated superior performance in diagnostic accuracy (pooled area under the receiver operating characteristic curve (AUC) 0.88, 95% confidence interval (CI): 0.85-0.91), triage efficiency (average wait time reduction: 18.7 minutes, 95% CI: 12.4-25.0), and outcome prediction (pooled sensitivity for hospital admission: 0.85, 95% CI: 0.81-0.89) compared to traditional methods. Conclusion: AI shows promise in improving ED decision-making processes. However, challenges remain in real-world implementation, ethical considerations, and long-term impact on patient outcomes. Future research should focus on large-scale validation studies and addressing ethical and safety concerns.