Abstract / Summary
Abstract Background: Artificial intelligence tools are being deployed in emergency departments quicker than systems of governance, training and validation can put in place to ensure their safe use. There is practically no evidence on the barriers experienced by emergency medicine practitioners in Iraq and there is no developed curriculum for AI safety training for those working in this specialty. Objectives: The purpose of this study is to identify the barriers to safe application of AI in emergency clinical decision making encountered by doctors in a private clinic in Baghdad, and to develop, implement and assess training activities aimed at overcoming these barriers. Methods: The study was an institutional three-phase multi-method research conducted between January and July 2026 in the emergency department of a private tertiary care hospital in Baghdad. Phase 1 consisted of surveys using cross-sectional established questionnaire (census of all 84 eligible emergency medicine practitioners) complemented with 16 semi-structured interviews. Phase 2 was based on applying Kern’s six-step strategy to create a curriculum based on the barriers identified. Phase 3 included educational program assessment based on a single-group pre-post design at 1st and 2nd Kirkpatrick stages with an -week follow-up and measurements for the effect of automation bias. Results: Seventy-six doctors took part in the study (90.5%). Whereas 63.2% of doctors claimed having practical experience in working with AI technology, only 11.8% reported having received any formal training. The barriers were grouped into the domains rated highest: knowledge and skills gaps (4.12/5), legal and liability issues (3.98) and data constraints/validity (3.94). Prior training and digital literacy negatively correlated with the barrier score. Five qualitative themes emerged with the most important ones being trust issues. In the study with 39 participating physicians, the mean knowledge score rose from 9.8 on a scale of 20 to 15.6 (p < 0.001), while the percentage of errors detected was reported as increasing from 41.0% to 79.5%; on the other hand, the percentage of physicians inclined to accept incorrect AI recommendations fell from 51.3% to 17.9%. Nevertheless, results persisted after eight weeks. Major conclusions regarding the barriers preventing safe AI use in this field are that they basically relate to educational, regulatory and contextual issues rather than technological ones. A short, barrier-specific simulation-based intervention caused considerable progress of physicians’ ability to detect and override unsafe AI recommendations. Keywords: artificial intelligence; emergency medicine; clinical decision-making; patient safety; automation bias; medical education; Iraq