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.

Fentanyl is a synthetic opioid widely used for its potent analgesic effects in chronic pain management and intraoperative anesthesia. However, its high potency, low cost, and accessibility have also made it a significant drug of abuse, contributing to the global opioid epidemic. This review aims to provide an in-depth analysis of fentanyl’s medical applications, pharmacokinetics, metabolism, and pharmacogenetics while examining its adverse effects and forensic implications. Special attention is given to its misuse, polydrug interactions, and the challenges in determining the cause of death in fentanyl-related fatalities. Fentanyl misuse has escalated dramatically, driven by its substitution for heroin and its availability through online platforms, including the dark web. Polydrug use, where fentanyl is combined with substances like xylazine, alcohol, benzodiazepines, or cocaine, exacerbates its toxicity and increases the risk of fatal outcomes. Fentanyl undergoes rapid distribution, metabolism by CYP3A4 into inactive metabolites, and renal excretion. Genetic polymorphisms in CYP3A4, OPRM1, and ABCB1 significantly influence individual responses to fentanyl, affecting its efficacy and potential for toxicity. Fentanyl’s side effects include respiratory depression, cardiac arrhythmias, gastrointestinal dysfunction, and neurocognitive impairments. Chronic misuse disrupts brain function, contributes to mental health disorders, and poses risks for younger and older populations alike. Fentanyl-related deaths require comprehensive forensic investigations, including judicial inspections, autopsies, and toxicological analyses. Additionally, the co-administration of xylazine presents distinct challenges for the scientific community. Histological and immunohistochemical studies are essential for understanding organ-specific damage, while pharmacogenetic testing can identify individual susceptibilities. The growing prevalence of fentanyl abuse highlights the need for robust forensic protocols, advanced research into its pharmacogenetic variability, and strategies to mitigate its misuse. International collaboration, public education, and harm reduction measures are critical for addressing the fentanyl crisis effectively.

Background/Objectives. Burnout and occupational stress are significant issues among forensic professionals, impacting their well-being and job performance. This systematic review aims to provide an up-to-date overview of the occupational stress and burnout experienced by forensic personnel, exploring the profound and multifaceted impact on their physical, mental, professional, and interpersonal well-being. Methods. A systematic review was conducted following PRISMA guidelines using Scopus and WOS databases to search for articles published from 1 January 2000 to 31 August 2024. The search used keywords related to burnout and forensic professions. Inclusion criteria were original articles in English and French, while reviews, book chapters, editorials, and notes were excluded. A total of 10 studies were included after eliminating duplicates and excluding irrelevant articles. Results. The review identified seven key findings. (1) High levels of occupational stress and burnout among forensic personnel necessitate effective stress management strategies and resilience training; (2) autopsy technicians in Romania experience burnout and alexithymia, particularly related to traumatic events involving children, highlighting the need for specialized support systems; (3) disparities in burnout and post-traumatic stress disorder (PTSD) symptoms were observed in autopsy technicians and resident doctors, suggesting tailored mental health resources; (4) organizational factors, such as peer support and compensation satisfaction, significantly impact burnout and secondary traumatic stress (STS) among sexual assault nurse examiners; (5) burnout among forensic physicians, both in Romania and Egypt, is linked to personality traits, job satisfaction, and socio-demographic factors; (6) pathologists face a range of health issues, including musculoskeletal problems and psychological disorders, underscoring the need for industry-specific health measures; and (7) the lack of wellness resources for forensic professionals calls for improved mental health support and training. Conclusions. The findings highlight the pervasive issue of burnout and stress among forensic professionals globally. Addressing these challenges requires comprehensive stress management programs, tailored mental health resources, and organizational support. Future research should focus on developing and implementing effective interventions to enhance resilience and job satisfaction within this high-stress field.

The integration of Artificial Intelligence (AI) into healthcare has revolutionized various aspects of clinical practice, including the management of medical malpractice disputes. AI-driven technologies, particularly machine learning (ML) and natural language processing (NLP), enable the automated analysis of electronic health records (EHRs) and other medical documentation, improving the efficiency, accuracy, and transparency of malpractice investigations. By systematically identifying inconsistencies, detecting patterns of errors, and evaluating compliance with clinical guidelines, AI systems offer valuable insights into potential negligence claims. This study examines the impact of AI on medical record management in malpractice disputes, addressing its role in mitigating human biases, enhancing forensic assessments, and supporting legal decision-making. AI-powered algorithms facilitate objective analysis by cross-referencing vast datasets of patient histories, diagnostic reports, and treatment protocols, thus strengthening the evidentiary basis for malpractice claims. However, despite its advantages, the use of AI in forensic and legal medicine raises significant ethical and legal concerns, including issues of accountability, data privacy, and algorithmic bias. Questions regarding liability in AI-assisted medical decision-making and the potential risk of over-reliance on automated assessments must be critically addressed. To maximize AI's benefits while minimizing risks, robust regulatory frameworks, interdisciplinary collaboration, and ethical oversight are essential. Ensuring transparency in AI-driven decision-making and safeguarding patient rights will be crucial in fostering trust in these technologies. The findings suggest that AI-assisted medical record analysis can significantly enhance dispute resolution processes by providing standardized, data-driven evaluations of malpractice claims, ultimately contributing to more equitable and efficient healthcare litigation.

Background/Objectives: Artificial intelligence (AI) is beginning to be applied in wound ballistics, showing preliminary potential to improve the accuracy and objectivity of forensic analyses. This review explores the current state of AI applications in forensic firearm wound analysis, emphasizing its potential to address challenges such as subjective interpretations and data heterogeneity. Methods: A systematic review adhering to PRISMA guidelines was conducted using databases such as Scopus and Web of Science. Keywords focused on AI and GSW classification identified 502 studies, narrowed down to 4 relevant articles after rigorous screening based on inclusion and exclusion criteria. Results: These studies examined the role of deep learning (DL) models in classifying GSWs by type, shooting distance, and entry or exit characteristics. The key findings demonstrated that DL models like TinyResNet, ResNet152, and ConvNext Tiny achieved accuracy ranging from 87.99% to 98%. Models were effective in tasks such as classifying GSWs and estimating shooting distances. However, most studies were exploratory in nature, with small sample sizes and, in some cases, reliance on animal models, which limits generalizability to real-world forensic scenarios. Conclusions: Comparisons with other forensic AI applications revealed that large, diverse datasets significantly enhance model performance. Transparent and interpretable AI systems utilizing techniques are essential for judicial acceptance and ethical compliance. Despite the encouraging results, the field remains in an early stage of development. Limitations highlight the need for standardized protocols, cross-institutional collaboration, and the integration of multimodal data for robust forensic AI systems. Future research should focus on overcoming current data and validation constraints, ensuring the ethical use of human forensic data, and developing AI tools that are scientifically sound and legally defensible.

Background/Objectives: Total hip arthroplasty (THA) is a widely performed procedure to alleviate pain and improve function in patients with hip disorders. However, leg length discrepancy (LLD) remains a prevalent complication. LLD can cause gait disturbances, back pain, postural imbalance, and patient dissatisfaction, along with significant medico-legal implications. This review examines the evaluation, management, and medico-legal aspects of LLD. Methods: The review analyzed literature on the prevalence, evaluation methods, and management strategies for LLD in THA. Radiographic and clinical assessment tools were considered, alongside factors such as pelvic obliquity and pre-existing conditions. The importance of preoperative planning, intraoperative techniques (including computer-assisted methods), and comprehensive documentation was evaluated to address both clinical and legal challenges. Results: The review shows that leg length discrepancy (LLD) following total hip arthroplasty (THA) occurs in 3% to 30% of cases, with mean values ranging from 3 to 17 mm. LLD may result from anatomical or procedural factors, and effective evaluation requires both radiographic imaging and clinical assessment. Preoperative planning plays a critical role in accurately assessing anatomical parameters and selecting appropriate prosthetic components to preserve or restore limb length symmetry. Advanced intraoperative techniques, including computer-assisted surgery, help reduce LLD incidence. While some complications may be unavoidable, proper documentation and patient communication, particularly regarding informed consent, are essential to mitigate medico-legal risks Conclusions: LLD after THA requires a multidimensional approach incorporating clinical, radiological, biomechanical, and legal considerations. Effective preoperative and intraoperative strategies, combined with robust communication and documentation, are essential to minimize LLD and its associated risks. A focus on precision and patient-centered care can improve outcomes and reduce litigation.