Search Results (2)

Background: Accurate estimation of the postmortem interval (PMI) is critical in forensic death investigations. Traditional signs of death—algor mortis, livor mortis, and rigor mortis—are generally reliable only within the first two to three days after death, with their accuracy decreasing as decomposition progresses. This paper presents a systematic review conducted in accordance with PRISMA guidelines, aiming to evaluate and compare current methods for estimating the PMI. Specifically, the study identifies both traditional and modern techniques, analyzes their advantages, limitations, and applicable timeframes, critically synthesizes the literature, and highlights the importance of combining multiple approaches to improve accuracy. Methods: A systematic search was conducted in the PubMed, Scopus, and Web of Science databases, following the PRISMA guidelines. The review included original articles and reviews that evaluated PMI estimation methods (through thanatological signs, entomology, microbial succession, molecular, imaging, and omics approaches). Extracted data included study design, methodology, PMI range, and accuracy information. Out of the 1245 identified records, 50 studies met the inclusion criteria for qualitative synthesis. Results: Emerging methods, such as molecular markers, microbial succession, omics technologies, and advanced imaging show improved accuracy across extended postmortem intervals. RNA degradation methods demonstrated higher accuracy within the first 72 h, while entomology and microbial analysis are more applicable during intermediate and late decomposition stages. Although no single method is universally reliable, combining traditional and modern approaches tailored to case-specific factors improves overall PMI estimation accuracy. Conclusions: This study supports the use of an integrative, multidisciplinary, and evidence-based approach to improve time-since-death estimation. Such a strategy enhances forensic outcomes by enabling more precise PMI estimates in complex or delayed cases, increasing legal reliability, and supporting court-admissible expert testimony based on validated, multi-method protocols. Full article

Diagnoses in forensic science cover many disciplinary and technical fields, including thanatology and clinical forensic medicine, as well as all the disciplines mobilized by these two major poles: criminalistics, ballistics, anthropology, entomology, genetics, etc. A diagnosis covers three major interrelated concepts: a categorization of pathologies (the diagnosis); a space of signs or symptoms; and the operation that makes it possible to match a set of signs to a category (the diagnostic approach). The generalization of digitization in all sectors of activity—including forensic science, the acculturation of our societies to data and digital devices, and the development of computing, storage, and data analysis capacities—constitutes a favorable context for the increasing adoption of artificial intelligence (AI). AI can intervene in the three terms of diagnosis: in the space of pathological categories, in the space of signs, and finally in the operation of matching between the two spaces. Its intervention can take several forms: it can improve the performance (accuracy, reliability, robustness, speed, etc.) of the diagnostic approach, better define or separate known diagnostic categories, or better associate known signs. But it can also bring new elements, beyond the mere improvement of performance: AI takes advantage of any data (data here extending the concept of symptoms and classic signs, coming either from the five senses of the human observer, amplified or not by technical means, or from complementary examination tools, such as imaging). Through its ability to associate varied and large-volume data sources, but also its ability to uncover unsuspected associations, AI may redefine diagnostic categories, use new signs, and implement new diagnostic approaches. We present in this article how AI is already mobilized in forensic science, according to an approach that focuses primarily on improving current techniques. We also look at the issues related to its generalization, the obstacles to its development and adoption, and the risks related to the use of AI in forensic diagnostics. Full article