Artificial intelligence in emergency medicine. A systematic literature review

Piliuk, Konstantin; Tomforde, Sven

doi:10.1016/j.ijmedinf.2023.105274

Cited by 7 publications

(2 citation statements)

References 284 publications

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“… The findings suggest that the choice of an AI tool for fracture analysis depends on the specific requirements for automation, ranging from automated classification to AI-assisted reading or minimizing false negatives. Diagnostics [ 12 ] Piliuk, Tomforde Artificial intelligence in emergency medicine. A systematic literature review.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review

Ventura,

Denton,

David

2024

MDER

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This study aimed to analyze the use of generative artificial intelligence in the emergency trauma care setting through a brief scoping review of literature published between 2014 and 2024. An exploration of the NCBI repository was performed using a search string of selected keywords that returned N=87 results; articles that met the inclusion criteria (n=28) were reviewed and analyzed. Heterogeneity sources were explored and identified by a significance threshold of P < 0.10 or an I 2 value exceeding 50%. If applicable, articles were categorized within three primary domains: triage, diagnostics, or treatment. Findings suggest that CNNs demonstrate strong diagnostic performance for diverse traumatic injuries, but generalized integration requires expanded prospective multi-center validation. Injury scoring models currently experience calibration gaps in mortality quantification and lesion localization that can undermine clinical utility by permitting false negatives. Triage predictive models now confront transparency, explainability, and healthcare ecosystem integration barriers limiting real-world translation. The most significant literature gap centers on treatment-oriented generative AI applications that provide real-time guidance for urgent trauma interventions rather than just analytical support.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review

Ventura,

Denton,

David

2024

MDER

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“…Algunos necesitan mejorar sus sistemas, tal y como se muestra en una revisión sistemática sobre la AI en los SEM en la que, de los 116 trabajos evaluados, solamente el 3% (4/116) presentaban una validación externa. Por ello, todavía hay desafíos significativos relacionados con la generalización de los estudios, la metodología, la validación externa y la calidad de los datos 11 . Algunos artículos acaban demostrando su utilidad teórica, pero a la hora de implementarlo en la práctica clínica, no demuestran una mejora significativa 12 .…”

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