Objective
This study aims to systematically review the ethical and legal discussions regarding the utilization of artificial intelligence (AI) for patient triage and resource allocation in emergency medicine, and to identify the current state of discussions, their limitations, and future research directions.
Methods
A comprehensive literature search was conducted following scoping review methodology. Relevant literature published after January 2020 was searched in the Web of Science, Scopus, CINAHL, PubMed, and Cochrane Library databases. Based on a PCC (population, concept, and context) framework (emergency patients/medical staff; triage, resource allocation; and emergency medicine with AI application), a final selection of 27 articles was analyzed.
Results
The selected literature raised various ethical and legal issues related to the introduction of AI triage systems and AI utilization in emergency medicine, including data privacy, algorithmic bias, automation dependency, accountability, and explainability. In response to these issues, human-centered design, implementation of explainable AI, establishment of regulatory frameworks, continuous verification and evaluation, and ensuring human-in-the-loop were discussed as major solutions. However, discussions on the risks of “persuasive AI” that could mislead users, ethical issues of generative AI, and social validation and patient and public involvement were found to be insufficient.
Conclusion
Ethical and legal discussions regarding AI in emergency medicine are evolving toward seeking concrete solutions at technical, institutional, and relational dimensions. However, in-depth research on ethical challenges, such as reflecting the specificity of rapidly developing AI and the values of emergency medicine, is urgently required.

Objective
This study evaluated the impact of head computed tomography (CT) on clinical decision-making about older adults with acute altered mental status (AMS) in the emergency department in terms of CT’s diagnostic yield, emergency department length of stay, and changes in medical strategy. It also attempted to find predictors of an acute imaging abnormality.
Methods
This was a 1-year, retrospective, single-center observational study of patients aged ≥75 years who underwent noncontrast head CT because of an isolated episode of AMS. The acute positive CT findings were ischemic strokes, hemorrhages, tumors, demyelinating lesions, hydrocephalus, and intracranial infections.
Results
A total of 594 CTs were performed, of which 38 (6.4%) were positive. The main etiology of AMS was sepsis (29.1%). Changes in medical strategy were more common in patients with a positive CT, and the major changes were ordering additional neuro exams (odds ratio [OR], 95.3; 95% confidence interval [CI], 38.4–233.8; P<0.001), adjusting treatments (OR, 12.2; 95% CI, 5.0–29.5; P<0.001), and referral to a neurologic unit (OR, 7.3; 95% CI, 3.0–17.5; P<0.01). Three factors were significantly associated with a positive outcome: Glasgow Coma Scale <13 (OR, 8.5; 95% CI, 2.3–28.9; P<0.001), head wound (OR, 3.1; 95% CI, 1.1–8.2; P=0.025), and dehydration (OR, 0.3; 95% CI, 0.1–0.4; P=0.021). For elderly patients with a Glasgow Coma Scale ≥13 and no head wound or clinical dehydration, the probability of a positive CT was 0.02 (95% CI, 0.01–0.04). Considering only those patients, the diagnostic yield fell to 1.7%.
Conclusion
In elderly patients, the causes of AMS are primarily extracerebral. Randomized clinical trials are needed to validate a clinical pathway for selecting patients who require emergent neuroimaging.