Background: Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention. Methods: We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs. Results: We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital’s HAI incidence from 1.31% to 0.58%. Conclusions: AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.

In forensic investigation, determining the time and cause of death becomes challenging, especially in cases where the remains are found in advanced decomposition, rendering traditional toxicological samples unavailable or unreliable. Entomotoxicology, an emerging methodology within forensic science, leverages insect specimens collected from cadavers as alternative toxicological samples. Several laboratory and field research studies have highlighted the efficacy in detecting various drugs, toxins, and elements absorbed by insects feeding on cadaveric tissues, even at low concentrations. However, correlation studies between drug concentrations in conventional matrices and insects remain controversial due to unknown factors influencing drug metabolism and larval feeding activity. This paper presents four real cases in which human cadavers were discovered in advanced stages of decomposition, and toxicological analyses were performed on both insect samples and available matrices. The results presented complement the scant literature currently available on the application of entomotoxicology in real cases, providing insights into the correlation between larvae and human specimen results. Furthermore, guidelines to collect and preserve entomological evidence at the crime scene and during the autopsy for use in entomotoxicological analyses are provided. This advancement holds promise in aiding forensic investigations, particularly in cases where traditional methods cannot be applied or require supporting data for further validation.

Ischemic heart disease is the main cause of death worldwide. Classic cardiac biomarkers, such as troponin, which are released due to myocyte necrosis, are widely used for diagnosis, but they provide limited information about the initial underlying cellular processes involved in myocardial infarction. Autophagy is now considered fundamental in the pathophysiology of cardiac ischemia and related reperfusion injury. This systematic review aims to identify and highlight candidate autophagy-related biomarkers in cardiac ischemia and infarction with potential benefits for early diagnosis, prognosis, and therapy. A comprehensive literature search was conducted up to 1 June 2025. We included studies that examined biomarkers involved in the autophagy process in cardiac ischemia/infarction, which involved humans and animal models. A total of 14 eligible articles were reviewed. Thirteen autophagy-related biomarkers were identified, including LC3-II/I, Beclin-1, ATG5, ATG7, p62, WIPI1, FGF21, CHRF, Rubicon, IL-1β, IL-18, and adiponectin. These biomarkers have a dynamic pattern, and they exhibited time-dependent changes during the different stages of myocardial infarction. Autophagy biomarkers present a promising understanding of the molecular mechanism of early myocardial ischemia and infarction. Integration of autophagy biomarkers with the classic markers should improve risk stratification, therapeutic decision-making, and prognosis in patients with ischemic heart disease.