Recent evolution in the field of data science has revealed the potential utility of machine learning (ML) applied to criminal justice. Hence, the literature focused on finding better techniques to predict criminal recidivism risk is rapidly flourishing. However, it is difficult to make a state of the art for the application of ML in recidivism prediction. In this systematic review, out of 79 studies from Scopus and PubMed online databases we selected, 12 studies that guarantee the replicability of the models across different datasets and their applicability to recidivism prediction. The different datasets and ML techniques used in each of the 12 studies have been compared using the two selected metrics. This study shows how each method applied achieves good performance, with an average score of 0.81 for ACC and 0.74 for AUC. This systematic review highlights key points that could allow criminal justice professionals to routinely exploit predictions of recidivism risk based on ML techniques. These include the presence of performance metrics, the use of transparent algorithms or explainable artificial intelligence (XAI) techniques, as well as the high quality of input data.

Background/Objectives: Artificial intelligence (AI) is revolutionizing the healthcare industry, improving diagnoses, treatments, and clinical processes. However, its integration poses significant cybersecurity risks, including data breaches, algorithmic opacity, and vulnerabilities in AI-controlled medical devices. This narrative review analyzes these threats and evaluates blockchain technology as a potential mitigation strategy within a Clinical Risk Management framework. Methods: The literature search was conducted on PubMed, Scopus, and Web of Science, considering peer-reviewed publications from 2000 to January 2025. 1,204 articles were identified. Inclusion criteria included studies on cybersecurity risks in healthcare, blockchain applications in the clinical setting, and regulatory references (eg, General Data Protection Regulation). Conference abstracts, non-English articles, and non-peer-reviewed contributions were excluded. To ensure methodological rigor, the Scale for the Assessment of Narrative Review Articles criteria were applied. Results: The thematic analysis highlighted recurring critical issues: difficulties with informed consent, unauthorized access to sensitive data, and systemic vulnerabilities in hospital digital infrastructures. Blockchain presents a promising solution thanks to its decentralization, immutability, and transparency. Integration with smart contracts enables dynamic consent management, secure data sharing, and real-time monitoring of medical devices. Permissioned networks improve traceability and regulatory compliance, while Layer 2 solutions and optimized consent protocols address scalability challenges. Conclusion: Despite its potential, blockchain adoption faces obstacles: high costs, regulatory rigidity, and poor acceptance among healthcare professionals. The review highlights the need for pilot projects, interdisciplinary collaboration, and regulatory updates for effective integration. Combining AI and blockchain in Clinical Risk Management can transform clinical risk management from reactive to proactive, improving patient safety, data governance, and accountability.

The COVID-19 pandemic raised concerns about the potential for co-infection or over-infection with other respiratory infections, as they can complicate the diagnosis, treatment and prognosis of the disease. This is also a challenge for forensic pathologists, who may come across cases where the presence of co-infection or over-infection is suspected or confirmed, and it is important that they take this into account when determining the cause of death. The aim of this systematic review is to analyse the prevalence of each specific pathogen co-infecting or over-infecting patients with SARS-CoV-2 infection. In total, 575 studies were selected from the Scopus and Pub-Med online databases and 8 studies were included in a meta-analysis. Male gender, advanced age and nursing home care are risk factors associated with the development of co-infection, whereas age, tachypnoea, hypoxaemia and bacterial infection are predictors of mortality. Overall, however, having a SARS-CoV-2 infection does not represent a real risk for the development of co-infections/super-infections.

The incorporation of artificial intelligence (AI) in health care offers revolutionary enhancements in patient diagnostics, clinical processes, and overall access to services. Nevertheless, this technological transition brings forth various new, intricate risks that pose challenges to current safety and ethical norms. This research explores the ability of enterprise risk management as an all‐encompassing framework to tackle these arising risks, providing both a forward‐looking and responsive strategy designed for the health care industry. At the core of this method are instruments that together seek to proactively uncover and address AI‐related weaknesses like algorithmic bias, system failures, and data privacy issues. On the reactive side, it incorporates incident reporting systems and root cause analysis, tools that enable health care providers to quickly address unexpected events and consistently improve AI implementation procedures. However, some application difficulties still exist. The unclear, “black box” characteristics of numerous AI models hinder transparency and responsibility, prompting inquiries about the clarity of AI‐generated choices and their adherence to ethical benchmarks in patient treatment. The research highlights that with the progress of AI technologies, the enterprise risk management framework also needs to evolve, addressing these new complexities while promoting a culture focused on safety in health care settings.

The syndemic framework proposed by the 2021–2030 World Health Organization (WHO) action plan for patient safety and the introduction of enabling technologies in health services involve a more effective interpretation of the data to understand causation. Based on the Systemic Theory, this communication proposes the “Systemic Clinical Risk Management” (SCRM) to improve the Quality of Care and Patient Safety. This is a new Clinical Risk Management model capable of developing the ability to observe and synthesize different elements in ways that lead to in-depth interventions to achieve solutions aligned with the sustainable development of health services. In order to avoid uncontrolled decision-making related to the use of enabling technologies, we devised an internal Learning Algorithm Risk Management (LARM) level based on a Bayesian approach. Moreover, according to the ethics of Job Well Done, the SCRM, instead of giving an opinion on events that have already occurred, proposes a bioethical co-working because it suggests the best way to act from a scientific point of view.

In the professional realm, the healthcare sector stands out as one of the most susceptible to violence. One notable manifestation of this is violence among colleagues, commonly referred to as horizontal violence, which has garnered significant attention in recent times. To delve deeper into this phenomenon across various categories of healthcare professionals, a comprehensive search was conducted on PubMed, Scopus, and CINAHL, resulting in the inclusion of 13 articles. The findings of this narrative review illuminate how horizontal violence can manifest in both physical and psychological forms and how it often becomes normalized among the healthcare professionals who endure it. Particularly vulnerable to such occurrences are recent graduates and those with limited professional experience. Furthermore, it has been observed that horizontal violence has detrimental effects on the well-being of those subjected to it, as well as on the quality of patient care delivered. Considering preventive measures, numerous studies emphasize the pivotal role of effective departmental leadership in fostering a harmonious work environment. Despite the largely underreported nature of this phenomenon, the conclusions drawn in this study advocate for a deeper exploration of the dynamics surrounding horizontal violence, with the goal of devising targeted strategies to mitigate its occurrence.