Machine Learning and Deep Learning for Predicting Traffic Crash Injury Severity: A Systematic Review and Meta-Analysis (2014-2025)

Machine learning and deep learning methods show promise for injury severity prediction, but comprehensive synthesis of their effectiveness, appropriate evaluation metrics, and optimal methodological approaches are lacking. This study was a systematic review and meta-analysis of machine learning and deep learning methods for predicting traffic crash injury severity conducted following PRISMA 2020 guidelines and TRIPOD+AI standards for prediction model reporting. Eligible studies were published between 2014 and 2025 that met the inclusion criteria of: observational studies using neural networks for crash injury severity prediction with reported F1-score, G-mean, sensitivity, or confusion matrix data. In total, 74 studies were analysed including 2,127,059 crash cases. Pooled macro F1-score was 78.6 percent (95% CI: 76.2-81.0%, I²=84%). Transfer learning achieved highest performance (83.2%), followed by transformer/LLM methods (84.7%), hybrid CNN-RNN (81.8%), RNN/LSTM (81.2%), CNN (79.5%), shallow neural networks (76.8%), and conventional machine learning (73.5%). Deep learning significantly outperformed conventional ML (pooled difference 7.7 percentage points, 95% CI: 4.9-10.5, p