Artificial intelligence in forensic science: Evaluation of ChatGPT for post-mortem interval estimation and Henssge nomogram use.

Authors

Elena Giovannini, Simone Santelli, Jacopo Lenzi, Guido Pelletti, Luca Berti, Arianna Giorgetti, Filippo Pirani, Susi Pelotti, Paolo Fais

Published in

Advances in clinical and experimental medicine : official organ Wroclaw Medical University. Aug 03, 2026. Epub Aug 03, 2026.

Abstract

In recent years, advancements in artificial intelligence (AI) have led to the creation of numerous large language models, including Chat Generative Pre-trained Transformer (ChatGPT), a natural language processing model developed by OpenAI. There has been increasing interest in exploring the potential of this tool, as evidenced by several studies in the healthcare domain that have investigated the performance of ChatGPT in responding to specific questions on relevant topics. However, its application in forensic contexts remains largely unexplored.
This study aims to evaluate the performance of ChatGPT in estimating the post-mortem interval (PMI) by considering thanatochronological changes as well as through the application of the Henssge nomogram.
Fifteen questions concerning PMI estimation were presented to ChatGPT. These questions were categorized into 3 domains, each addressing different aspects: 1) theoretical notions, 2) practical application of theoretical notions, and 3) utilization of the Henssge nomogram. The evaluation of responses included 3 sub-criteria: focus, accuracy, and completeness.
The results showed a high level of accuracy and completeness in addressing theoretical issues. However, the AI failed when responding to practical case scenarios and when calculating PMI using the Henssge nomogram.
Although AI may be attractive for application to forensic science questions, uncertain source documents and incomplete access to the scientific literature may affect accuracy. The study concluded that AI should be investigated for use in forensic science; however, it is currently not suitable for practical PMI estimation.

PMID:
42543033
Bibliographic data and abstract were imported from PubMed on 03 Aug 2026.

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