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Updated: Jun 13, 2026

Abbiategrasso Brain Bank Protocol for Collecting, Processing and Characterizing Aging Brains
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Artificial intelligence in forensic science: a systematic review. Part II: long-range postmortem interval estimation.

Valentina Bugelli1, Francesco Calabrò1, Jessika Camatti2

  • 1University of Parma, Parma, Italy.

International Journal of Legal Medicine
|June 11, 2026
PubMed
Summary

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This summary is machine-generated.

Artificial intelligence (AI) shows promise for improving postmortem interval (PMI) estimation by analyzing complex forensic data. However, current AI models for PMI prediction need more standardized validation and larger datasets for reliable application.

Area of Science:

  • Forensic Medicine
  • Computational Biology
  • Biotechnology

Background:

  • Postmortem interval (PMI) estimation is crucial in forensic science but remains challenging due to decomposition complexity.
  • Artificial intelligence (AI) and machine learning (ML) offer novel approaches to enhance PMI prediction accuracy.

Purpose of the Study:

  • To systematically review and evaluate existing AI-based models for postmortem interval estimation.
  • To assess the evidence base, methodologies, and performance of AI applications in forensic PMI prediction.

Main Methods:

  • A systematic literature search was performed in PubMed/MEDLINE and Scopus up to March 1, 2026, adhering to PRISMA 2020 guidelines.
  • Studies utilizing AI, ML, or deep learning for PMI estimation on real postmortem data were included.
Keywords:
Artificial intelligenceDecompositionForensic imagingForensic pathologyMachine learningPostmortem interval

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  • Data extraction focused on study characteristics, data types, AI architectures, validation, and performance metrics.

    • Main Results:

    • Sixty-four studies met the inclusion criteria, with microbiome-based models being most common (n=29), followed by metabolomics/proteomics (n=11) and imaging (n=11).
    • Random Forest algorithms were frequently employed, especially in microbiome studies.
    • Predictive performance varied significantly, with some models demonstrating high accuracy for specific data types and PMI ranges.

    Conclusions:

    • AI holds significant potential to improve the accuracy and objectivity of PMI estimation by integrating diverse forensic data.
    • Current limitations include methodological heterogeneity, small sample sizes, and insufficient external validation.
    • Future research should prioritize large, multicenter datasets, standardized validation, and multimodal AI approaches for robust forensic applications.