ArticleEmergency medicine journal : EMJ2025
Evaluating the impact of AI assistance on decision-making in emergency doctors interpreting chest X-rays: a multi-reader multi-case study.
Article in Emergency medicine journal : EMJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence as a second reader for pneumothorax detection: redirecting rather than reducing chest CT use.BMC emergency medicine · 2026Article
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10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) tools could assist emergency doctors interpreting chest X-rays to inform urgent care. However, the impact of AI assistance on clinical decision-making, a precursor to enhanced care and patient outcomes, remains understudied. This study evaluates the effect of AI assistance on clinical decisions of emergency doctors interpreting chest X-rays.
methodJunior and senior residents, emergency registrars and consultants working in Australian emergency departments were eligible. Doctors completed 18 clinical vignettes involving chest X-ray interpretation, representative of typical patient presentations. Vignettes were randomly selected from a bank of 49 based on the emergency medicine curriculum and contained a chest X-ray, presenting complaint, relevant symptoms and observations. Of the 18 vignettes, each doctor was randomly assigned to have half assisted by a commercial AI tool capable of detecting 124 different chest X-ray findings. Four vignettes contained X-rays known to produce incorrect AI findings. Primary outcomes were correct diagnosis and patient management. X-ray interpretation time, confidence of diagnosis, perceptions about the AI tool and the differential impact of AI assistance by seniority were also examined.
results200 doctors participated. AI assistance increased correct diagnosis by 5.9% (95% CI 2.7 to 9.2%) compared with unassisted vignettes, with the largest increase among senior residents (11.8%; 95% CI 5.2% to 18.3%). Patient management increased by 3.2% (95% CI 0.1% to 6.4%). Confidence in diagnosis increased by 5% (95% CI 3.4% to 6.6%; p<0.001) and interpretation time increased by 4.9 s (p=0.08). Incorrect AI findings decreased correct diagnosis by 1% for false-positive (p=0.9) and 9% for false-negative findings (p=0.1). Participants found the AI tool helpful for interpreting chest X-rays, highlighting missed findings, but were neutral on its accuracy.
conclusionImprovements in diagnosis and patient management without meaningful increases in interpretation time suggest AI assistance could benefit clinical decisions involving chest X-ray interpretation. Further studies are required to ascertain if such improvements translate to improved patient care.
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