Health risks within prisons are well known and have worsened with the 2019 coronavirus pandemic (COVID-19), becoming a public health emergency. To date, there are more than 10 million inmates in the world; in most cases, conditions are bad and health care is scarce. A SARS-CoV-2 outbreak inside a prison is extremely rapid. The aim of this systematic review was to analyze all possible prevention techniques to reduce the risk of COVID-19 related infection within prisons. A systematic review of the literature was performed according to the PRISMA guidelines. Scopus, Web of Science, PubMed, and Google Scholar were used as search engines from 1 January 2020 to 1 November 2021 to evaluate the prevention of COVID-19 in prisoners. A total of 1757 articles were collected. Of them, 486 duplicates were removed. A total of 1250 articles did not meet the inclusion criteria. In conclusion, 21 articles were included in the present systematic review. From this analysis, it emerged that the most common COVID-19 prevention methods were the screening of the entire population (prisoners and workers) inside the prison through swab analysis and the reduction in overcrowding in prisons. Few studies concerned the prevention of COVID-19 infection through vaccination and the implementation of quarantine. To our knowledge, this is the first systematic review that evaluates the prevention of COVID-19 within jails and the real effectiveness of all possible methods used and published in the literature. Finally, a very useful strategic protocol is provided to reduce the incidence of infection and to control and manage COVID-19 in prisons.

Traumatic brain injury (TBI) represents a major public health problem, being a leading cause of disability and mortality among young people in developed countries. Head trauma occurs across all age groups, each experiencing consistently high rates of mortality and disability. This review aims to present an overview of TBI epidemiology and its socioeconomic impact, alongside data valuable for prevention, clinical management, and research efforts. Methods: A narrative review of TBI was performed with a particular focus on forensic pathology and public health. In fact, this review highlighted the economic and epidemiological aspects of TBI, as well as autopsy, histology, immunohistochemistry, and miRNA. Results: These data, together with immunohistochemical markers, are crucial for histopathological diagnosis and to determine the timing of injury onset, a fundamental aspect in forensic pathology practice. There is compelling evidence that brain injury biomarkers may enhance predictive models for clinical and prognostic outcomes. By clarifying the cause of death and providing details on survival time after trauma, forensic tools offer valuable information to improve the clinical management of TBI and guide preventive interventions. Conclusions: TBI is one of the most common causes of death today, with high costs for health care spending. Knowing the different mechanisms of TBI, reduces health care costs and helps improve prognosis.

Background/Objectives: Accurate post-mortem interval (PMI) estimation becomes increasingly difficult when bodies decompose under extreme heat. Hyperthermal Mediterranean environments accelerate soft-tissue degradation, induce early mummification, and distort classical thanatological indicators, often resulting in substantial PMI overestimation. This study analyzes three forensic cases affected by climate-driven decomposition anomalies and presents a climate-adaptive, AI-assisted diagnostic framework applied uniformly across all cases to improve PMI interpretation. Methods: A retrospective case series analysis was conducted on three individuals recovered during summer heatwaves. Crime scene investigation, post-mortem computed tomography (PMCT), autopsy, and genetic identification were integrated with 5–15-year meteorological datasets. Classical PMI estimations were compared with circumstantial data. A multimodal AI model, incorporating environmental features, decomposition morphology, and microenvironmental modifiers, was operationalized for each case using a hybrid Random Forest–LSTM architecture. Engineered indices included Accumulated Degree Days (ADD), a Decomposition Index, and climate-stress metrics (Thermal Load Index, Desiccation Pressure Factor, Microenvironmental Distortion Coefficient). Quantile regression provided calibrated prediction intervals. Results: Morphological assessments overestimated PMI in every case, suggesting intervals of 1–6 months despite true PMIs of approximately 20 days (Cases 1–2) or 36–48 h (Case 3). The AI model yielded conceptual outputs more consistent with verified PMIs, ~21 days (Case 1), ~23 days (Case 2), and ~42 h (Case 3), each accompanied by 50% and 90% prediction intervals. Explainability analyses identified thermal load, desiccation pressure, and microenvironmental distortion, particularly insulation in Case 3, as dominant drivers. Conclusions: Extreme heat fundamentally alters decomposition trajectories, rendering classical PMI methods unreliable. Applying a climate-aware, AI-assisted diagnostic framework across all three cases improved interpretability, providing uncertainty-aware estimates aligned with true PMIs. The AI framework is presented as a conceptual, non-trained, proof-of-concept system, and reported outputs represent operational demonstrations rather than validated predictions, offering a promising foundation for next-generation PMI diagnostics in hyperthermal forensic settings.

Background: Health assessment in custodial settings represents a core medico-legal function, particularly when clinical changes may influence detention compatibility and judicial decisions. Objective documentation of disease progression during incarceration is essential to ensure respect for the principle of healthcare equivalence and the protection of fundamental rights. Methods: A retrospective descriptive medico-legal analysis was conducted on 37 male inmates referred for forensic evaluation. Standardized assessments included anamnesis, physical and psychiatric examination, evaluation of medical records, and comparative evaluation of pre-detention and detention-period health status. Results: Pre-existing conditions accounted for 73% of pathologies, while 27% developed during imprisonment. Clinical deterioration occurred in 35.1% of inmates (13 cases). Psychiatric disorders were frequent (27%, 10 inmates), predominantly depressive. Conclusions: The findings indicate that imprisonment may constitute a setting that contributes to the progression of medical and psychiatric conditions, highlighting structural and organizational challenges within penitentiary healthcare. Strengthening standardized medico-legal assessment protocols, improving multidisciplinary collaboration, and ensuring continuity of care between prison and community health services are crucial steps toward protecting inmates’ health. This study provides an exploratory assessment of health conditions in a cohort of inmates within the Italian correctional system, emphasizing the central role of healthcare in custodial settings. Since many Italian correctional facilities are equipped with well-developed healthcare services and appropriate diagnostic and therapeutic resources, numerous acute and chronic medical conditions can be effectively managed within the prison setting, ensuring continuity of care while limiting the need for transfer to external healthcare facilities.

Background: Healthcare liability litigation has increased substantially, highlighting the need for structured and evidence-based medico-legal evaluations. While generative artificial intelligence (AI) has been extensively studied in clinical medicine, its application in medico-legal consultancy remains limited. Objective: To assess whether two customized GPT-based models, simulating opposing medico-legal perspectives (“patient-oriented” and “hospital-oriented”), can analyze real clinical documentation and generate reports comparable to those produced by human experts. Methods: Ten real healthcare liability cases were retrospectively analyzed. For each case, complete anonymized clinical records and human-authored medico-legal reports were available. Two GPT-based models were configured using domain-specific instructions and authoritative medico-legal references, without retraining. Both models generated simulated reports from identical documentation. Outputs were evaluated using structured qualitative scores assessing argumentative coherence, clinical synthesis, bibliographic accuracy and relevance, and concordance of permanent impairment estimates. Results: No significant difference in overall argumentative coherence was observed between models (Wilcoxon test, p = 0.844). Orientation-consistent trends emerged after stratification by liability status. Agreement was substantial for clinical synthesis (Cohen’s κ = 0.804) but slight for permanent impairment estimates (κ = 0.167). The patient-oriented model produced significantly more fabricated or non-verifiable references (OR = 3.74; p = 0.0065), while citation relevance did not differ significantly between models ( p = 0.195). Conclusions: Generative AI can simulate divergent medico-legal reasoning and rapidly produce structured reports. However, expert human oversight remains essential, particularly for source verification and medico-legal interpretation.