Artificial intelligence in forensic science: a systematic review. Part II: long-range postmortem interval estimation

Postmortem interval (PMI) estimation remains a major challenge in forensic medicine due to the complex and multifactorial nature of decomposition processes. In recent years, artificial intelligence (AI) and machine learning techniques have been increasingly applied to improve PMI prediction. This systematic review aimed to evaluate the current evidence on AI-based models developed for PMI estimation. A systematic literature search was conducted in PubMed/MEDLINE and Scopus from database inception to 1 March 2026, following PRISMA 2020 guidelines. Studies were included if they applied AI, machine learning, or deep learning methods to estimate PMI using real postmortem datasets. Data extraction included study characteristics, data modality, AI model architecture, validation strategy, and reported performance metrics. A total of 64 studies met the inclusion criteria. The most common approach involved microbiome-based models ( n = 29), followed by metabolomics and proteomics approaches ( n = 11) and imaging-based AI models ( n = 11). Random Forest algorithms were the most frequently used machine learning method, particularly in microbiome studies. Reported predictive performance varied widely across studies, with several models achieving high accuracy or low prediction errors depending on the data modality and PMI range investigated. AI represents a promising tool for improving the accuracy and objectivity of PMI estimation by enabling the integration of complex forensic datasets. However, current evidence is limited by heterogeneous methodologies, small datasets, and a lack of external validation. Future research should focus on large multicenter datasets, standardized validation protocols, and multimodal AI models integrating diverse forensic data sources.