Conclusions

Artificial intelligence has emerged as a powerful tool for improving the estimation of the postmortem interval by enabling the analysis of complex biological, molecular, and environmental datasets. The studies included in this systematic review demonstrate that AI-based approaches—particularly those based on microbial succession, molecular biomarkers, imaging data, and spectroscopic techniques—can achieve promising predictive performance across different PMI ranges.

Among the investigated strategies, microbiome-based models represent the most extensively explored approach, reflecting the strong association between microbial community dynamics and decomposition processes. At the same time, recent developments in metabolomics, proteomics, and deep learning–based imaging analysis highlight the growing diversification of data sources used for PMI prediction.

Despite these advances, several challenges remain. Many studies rely on small experimental datasets, limited external validation, and animal models that may not fully reproduce real forensic scenarios. In addition, the heterogeneity of analytical pipelines and performance metrics currently limits direct comparisons between studies.

Future research should therefore focus on the development of larger, standardized datasets derived from human forensic cases, the implementation of robust external validation strategies, and the integration of multimodal datasets combining biological, molecular, imaging, and environmental information. Such approaches may enable the development of more accurate, generalizable, and interpretable AI systems for forensic PMI estimation.

Overall, artificial intelligence represents a promising frontier in forensic science and has the potential to significantly improve the objectivity and reliability of postmortem interval estimation in future forensic investigations.