Background/Objectives: Postmortem microbiology has traditionally been regarded with caution in forensic practice due to concerns related to contamination, bacterial translocation, and postmortem microbial overgrowth. As a result, microbiological findings obtained after death have often been considered unreliable or of limited diagnostic value. However, growing evidence indicates that, when appropriately interpreted and integrated with autopsy findings, histopathology, and circumstantial information, postmortem microbiology can provide crucial support for cause-of-death determination. This narrative review critically examines the current role of postmortem microbiology in forensic diagnostics, with a focus on its diagnostic applications, interpretative challenges, and future perspectives. Methods/Results: The transition from conventional culture-based techniques to molecular approaches—including polymerase chain reaction, microbiome analysis, and metagenomic methods—is discussed, highlighting both their potential advantages and inherent limitations within the forensic setting. Particular attention is devoted to key interpretative issues such as postmortem interval, sampling strategies, contamination, and bacterial translocation. In addition to cause-of-death attribution, emerging applications—including postmortem interval estimation, trace evidence analysis, and artificial intelligence–based models—are reviewed. Although these approaches show promising research potential, their routine forensic applicability remains limited by methodological heterogeneity, lack of standardization, and interpretative complexity. Conclusions: In conclusion, postmortem microbiology represents a valuable diagnostic tool when applied within a multidisciplinary forensic framework. Its effective use requires cautious interpretation and integration with pathological and contextual evidence, avoiding standalone or automated conclusions. Future progress will depend on standardized methodologies, multidisciplinary collaboration, and a clear distinction between experimental research and routine forensic practice.

The importance of non-human DNA in the forensic field has increased greatly in recent years, together with the type of applications. The molecular species identification of animal and botanical material may be crucial both for wildlife trafficking and crime scene investigation. However, especially for forensic botany, several challenges slow down the implementation of the discipline in the routine.Although the importance of molecular analysis of animal origin samples is widely recognized and the same value is acknowledged to the botanical counterpart, the latter does not find the same degree of application.The availability of molecular methods, especially useful in cases where the material is fragmented, scarce or spoiled preventing the morphological identification, is not well known. This work is intended to reaffirm the relevance of non-human forensic genetics (NHFG), highlighting differences, benefits and pitfalls of the current most common molecular analysis workflow for animal and botanical samples, giving a practical guide. A flowchart describing the analysis paths, divided in three major working areas (inspection and sampling, molecular analysis, data processing and interpretation), is provided. More real casework examples of the utility of non-human evidence in forensic investigations should be shared by the scientific community, especially for plants. Moreover, concrete efforts to encourage initiatives in order to promote quality and standardization in the NHFG field are also needed.

The European Council of Legal Medicine (ECLM) on-site inspection form allows the collection of data relevant to the identification, collection, sampling and preservation of all elements that, during the on-site inspection, may be useful as forensic evidence. The aim of this study is to assess the completeness of the information collected in a large number of on-site inspections, the records of which were drawn up without the use of the ECLM on-site inspection form, in order to verify the usefulness and the application of this form. The authors present a multicentre study involving 20 Italian forensic medicine institutes. For each institute, the reports of on-site inspections carried out without the use of the ECLM on-site inspection form were collected and a forensic pathologist was asked to analyse the individual cases and to identify, for each case, the presence or absence of information regarding all the points examined by the ECLM on-site inspection form. A practical database was prepared and sent to each institute for this analysis. Data were collected and analysed from a total of 1721 on-site inspection reports. Our results document that certain items on the ECLM on-site inspection form are not always investigated in reports written without the use of this tool. The use of the ECLM on-site inspection form proves to be a valid tool to assist the forensic pathologist during the on-site inspection. We therefore hope that the forms developed by the ECLM will be systematically adopted in the forensic practice of the forensic pathologist.

The rapid integration of Artificial Intelligence (AI) into healthcare promises significant benefits but also raises unprecedented ethical, clinical, and legal challenges. Current medico-legal frameworks, primarily designed for human decision-making, are often inadequate to address liability issues arising from algorithmic errors or opaque "black box" models. This paper introduces a novel medico-legal methodology that combines proactive and reactive approaches to risk assessment, originally developed within European forensic medicine, and adapts it to the context of AI in healthcare. By systematically analyzing data collection, dataset validation, error identification, and causal reconstruction, the proposed framework provides a structured path for evaluating medical liability when AI systems are involved. This dual approach not only supports clinicians, developers, and policymakers in preventing harm, but also establishes a robust forensic tool for liability assessment. The methodology offers a step toward internationally applicable standards for addressing the medico-legal implications of AI in medicine.