Background: Healthcare systems represent complex organizations within which multiple factors (physical environment, human factor, technological devices, quality of care) interconnect to form a dense network whose imbalance is potentially able to compromise patient safety. In this scenario, the need for hospitals to expand reactive and proactive clinical risk management programs is easily understood, and artificial intelligence fits well in this context. This systematic review aims to investigate the state of the art regarding the impact of AI on clinical risk management processes. To simplify the analysis of the review outcomes and to motivate future standardized comparisons with any subsequent studies, the findings of the present review will be grouped according to the possibility of applying AI in the prevention of the different incident type groups as defined by the ICPS. Materials and Methods: On 3 November 2023, a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was carried out using the SCOPUS and Medline (via PubMed) databases. A total of 297 articles were identified. After the selection process, 36 articles were included in the present systematic review. Results and Discussion: The studies included in this review allowed for the identification of three main “incident type” domains: clinical process, healthcare-associated infection, and medication. Another relevant application of AI in clinical risk management concerns the topic of incident reporting. Conclusions: This review highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors. It appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills. To facilitate the analysis of the present review outcome and to enable comparison with future systematic reviews, it was deemed useful to refer to a pre-existing taxonomy for the identification of adverse events. However, the results of the present study highlighted the usefulness of AI not only for risk prevention in clinical practice, but also in improving the use of an essential risk identification tool, which is incident reporting. For this reason, the taxonomy of the areas of application of AI to clinical risk processes should include an additional class relating to risk identification and analysis tools. For this purpose, it was considered convenient to use ICPS classification.

Background and aims: The gut microbiota is a complex ecosystem containing bacteria, viruses, fungi, yeasts and other single-celled organisms. It is involved in the development and maintenance of both innate and systemic immunity of the body. Emerging evidence has shown its role in liver diseases through the immune system cross-talk. We review herein literature data regarding the triangular interaction between gut microbiota, immune system and liver in health and disease. Methods: We conducted a search on the main medical databases for original articles, reviews, meta-analyses, randomized clinical trials and case series using the following keywords and acronyms and their associations: gut microbiota, microbiome, gut virome, immunity, gastrointestinal-associated lymphoid tissue (GALT), non-alcoholic fatty liver disease (NAFLD), non-alcoholic steato-hepatitis (NASH), alcoholic liver disease, liver cirrhosis, hepatocellular carcinoma. Results: The gut microbiota consists of microorganisms that educate our systemic immunity through GALT and non-GALT interactions. The latter maintain health but are also involved in the pathophysiology and in the outcome of several liver diseases, particularly those with metabolic, toxic or immune-mediated etiology. In this context, gut virome has an emerging role in liver diseases and needs to be further investigated, especially due to the link reported between severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection and hepatic dysfunctions. Conclusions: Changes in gut microbiota composition and alterations in the immune system response are involved in the pathogenesis of metabolic and immune-mediated liver diseases.

Context.—: The novel coronavirus disease 2019 (COVID-19) pandemic is significantly changing methodologic approaches in all branches of the health system. From a forensic point of view, this event is partly changing the manner in which forensic pathologists and all those who work in autopsy services operate, but above all, it is changing the patterns established for years by which cadavers are analyzed postmortem. Objective.—: To present a review of the literature and a proposal for COVID-19 autopsy protocols. To contain the infection risk, a revision of all the protocols that until now have been applied to the examination of bodies that require autopsy services is required. Data Sources.—: Currently, the diagnosis and postmortem analysis of positive or suspected COVID-19 cases plays a crucial role in scientific research. A review of the main recommendations proposed by international scientific societies regarding the risk of infection during autopsy was carried out. Scientific papers currently available via the PubMed NCBI search engine on COVID-19 postmortem diagnosis were also examined. Conclusions.—: Throughout the history of medicine, autopsy has been fundamental to the understanding of multiple pathogenic processes that are investigated postmortem. The purpose of the study is to propose an operating protocol that can be useful for all clinical and forensic autopsies, with particular reference to the correct methods to be applied to the examination of positive or suspected COVID-19 cases, regarding both the autopsy procedure and the collection and analysis of biological samples.

Estimating the post-mortem interval (PMI) is a very complex issue due to numerous variables that may affect the calculation. Several authors have investigated the quantitative and qualitative variations of protein expression on post-mortem biological samples in certain time intervals, both in animals and in humans. However, the literature data are very numerous and often inhomogeneous, with different models, tissues and proteins evaluated, such that the practical application of these methods is limited to date. The aim of this paper was to offer an organic view of the state of the art about post-mortem protein alterations for the calculation of PMI through the analysis of the various experimental models proposed. The purpose was to investigate the validity of some proteins as “molecular clocks” candidates, focusing on the evidence obtained in the early, intermediate and late post-mortem interval. This study demonstrates how the study of post-mortem protein alterations may be useful for estimating the PMI, although there are still technical limits, especially in the experimental models performed on humans. We suggest a protocol to homogenize the study of future experimental models, with a view to the next concrete application of these methods also at the crime scene.

The phenomenon of suicide is a much studied but still little-known issue. In this particular period of health emergency, quarantine and mandatory restrictions could play a role in the genesis of fatal events or suicide attempts not only in people at risk. However, this issue has not yet been adequately addressed in the literature. The influence of the global pandemic could change the way suicide cases are analyzed; in the future, it is necessary to reconsider and analyze the various risk groups by population but above all to evaluate new methods of intervention for avoiding the increase in fatal events related to the current emergency whose duration is still unknown. (PsycInfo Database Record (c) 2020 APA, all rights reserved).