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TRV-2026-0729Certified recordPeer-reviewed

Mortality prediction of road traffic crash with artificial intelligence: a systematic review

Background Road traffic crashes cause substantial global mortality and disability. Conventional injury severity scores may not fully capture the complex interactions among demographic, clinical, crash and environmental factors. Artificial intelligence and machine learning may improve mortality prediction by modelling non-linear patterns in traffic crash data. Methods This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidance. PubMed/MEDLINE, Web of S…

Health · The Trace — both readings · certified 2026-08-10 · v1 · article view · machine-readable

Current reading — gain

Systematic review found AI and machine learning may improve mortality prediction after road traffic crashes by modelling non-linear patterns among demographic, clinical and crash factors.

Current reading — problem

Clinical translation of AI mortality prediction after road traffic crashes remains limited by insufficient external validation, inconsistent handling of class imbalance, and incomplete reporting of tuning and missing data strategies.

What this doesn’t fix

Findings were synthesised narratively due to heterogeneity, and only two studies reported external validation, limiting generalizability.

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Truvace Impact Record TRV-2026-0729, v1: “Mortality prediction of road traffic crash with artificial intelligence: a systematic review.” Truvace, 2026-08-10. /record/TRV-2026-0729 (accessed at citation time). sha256 b61e2f507dfc6340…

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