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: Forensic DNA phenotyping (FDP) enables the prediction of externally visible characteristics (EVCs) such as eye, hair, and skin color, ancestry, and age from biological traces. However, low template DNA (LT-DNA), often derived from degraded or trace samples, poses significant challenges due to allelic dropout, contamination, and incomplete profiles. This review evaluates recent advances in FDP from LT-DNA, focusing on the integration of machine learning (ML) models to improve predictive accuracy and operational readiness, while addressing ethical and population-related considerations. Methods: A comprehensive literature review was conducted on FDP and ML applications in forensic genomics. Key areas examined include SNP-based trait modeling, genotype imputation, epigenetic age estimation, and probabilistic inference. Comparative performance of ML algorithms (Random Forests, Support Vector Machines, Gradient Boosting, and deep learning) was assessed using datasets such as the 1000 Genomes Project, UK Biobank, and forensic casework samples. Ethical frameworks and validation standards were also analyzed. Results: ML approaches significantly enhance phenotype prediction from LT-DNA, achieving AUC > 0.9 for eye color and improving SNP recovery by up to 15% through imputation. Tools like HIrisPlex-S and VISAGE panels remain robust for eye and hair color, with moderate accuracy for skin tone and emerging capabilities for age and facial morphology. Limitations persist in admixed populations and traits with polygenic complexity. Interpretability and bias mitigation remain critical for forensic admissibility. Conclusions: L integration strengthens FDP from LT-DNA, offering valuable investigative leads in challenging scenarios. Future directions include multi-omics integration, portable sequencing platforms, inclusive reference datasets, and explainable AI to ensure accuracy, transparency, and ethical compliance in forensic applications.

Background: Anabolic–androgenic steroids (AASs) are commonly used for performance enhancement but have been linked to significant neurobiological consequences. This review explores the impact of AASs on neurochemical pathways, cognitive function, and psychiatric disorders, highlighting their potential neurotoxicity. Methods: A narrative review of current literature was conducted to examine AASs-induced alterations in neurotransmitter systems, structural and functional brain changes, and associated psychiatric conditions. The interplay between AASs use and other substances was also considered. Results: Chronic AASs exposure affects serotonin and dopamine systems, contributing to mood disorders, aggression, and cognitive deficits. Structural and functional changes in the prefrontal cortex and limbic regions suggest long-term neurotoxicity. AASs use is associated with increased risks of depression, anxiety, and psychosis, potentially driven by hormonal dysregulation and neuroinflammation. Co-occurring substance use exacerbates neurocognitive impairments and behavioral disturbances. Discussion: While evidence supports the link between AASs use and neurotoxicity, gaps remain in understanding the precise mechanisms and long-term effects. Identifying biomarkers of brain damage and developing targeted interventions are crucial for mitigating risks. Increased awareness among medical professionals and policymakers is essential to address AASs-related neuropsychiatric consequences. Conclusions: AASs abuse poses significant risks to brain health, necessitating further research and prevention efforts. Evidence-based strategies are needed to educate the public, enhance early detection, and develop effective interventions to reduce the neuropsychiatric burden of AASs use.

Background: The COVID-19 pandemic significantly disrupted forensic science, exposing vulnerabilities and introducing unprecedented challenges. Five years later, its impact persists, necessitating ongoing adaptations in forensic practice. This study examines key transformations, persistent issues, and emerging challenges in forensic science post-pandemic. Methods: A critical analysis of forensic science’s response to the pandemic was conducted, focusing on operational disruptions, methodological advancements, educational shifts, and technological integration. Results: Forensic operations faced delays due to case backlogs, restricted in-person work, and postponed court proceedings. Forensic pathology evolved with increased reliance on molecular autopsy techniques to clarify COVID-19-related deaths. Educational methods shifted toward virtual learning, prompting discussions on standardized digital training. Additionally, artificial intelligence and automation gained prominence in forensic investigations, enhancing crime scene analysis and predictive modeling. Discussion: While forensic science demonstrated adaptability, challenges remain in international collaboration, resource distribution, and professional training. The pandemic accelerated technological integration but also raised ethical and procedural concerns, particularly regarding AI applications in legal contexts. Virtual learning innovations necessitate further development to ensure competency in forensic training. Conclusions: Forensic science continues to evolve in response to post-pandemic realities. Addressing gaps in cooperation, technology implementation, and training will be crucial to strengthening the field. By assessing these changes, this study underscores forensic science’s resilience and adaptability, offering insights into its future trajectory amid ongoing challenges.

Fatal dog attacks are rare but devastating events with significant public health, forensic, and legal implications. Italy lacks a centralized registry for such incidents, limiting the ability to monitor trends and implement preventive strategies. This retrospective study analyzes all verified fatal dog attacks in Italy from 2009 to 2025. Data were collected from national and regional media, cross-verified, and organized into a comprehensive database. Descriptive statistics, chi-square tests, logistic and Poisson regressions, and interaction analyses were performed to identify patterns in victim demographics, breed involvement, ownership status, and environmental context. A total of 54 fatal attacks were recorded, with an increasing trend observed in the last five years. Elderly individuals (≥65) and preschool-aged children (≤4) were the most affected groups. Molosser and bull-type breeds were implicated in 69% of cases, and 92.6% of attacks involved owned dogs—more than half belonging to the victim. Private settings accounted for 66.7% of incidents. Comparative analysis with U.S. data revealed similar demographic and breed-related patterns, but also highlighted Italy’s lack of a centralized behavioral risk registry. Fatal dog attacks in Italy follow recurring and preventable patterns. The absence of a national database severely limits surveillance and intervention. A centralized behavioral risk registry, modeled on international systems, should be established to support early detection, policy development, and multidisciplinary collaboration.

Fatal dog attacks are rare but devastating events with significant public health, forensic, and legal implications. Italy lacks a centralized registry for such incidents, limiting its ability to monitor trends and implement preventive strategies. This retrospective study analyzes all verified fatal dog attacks in Italy from 2009 to 2025. Data were collected from national and regional media, cross-verified, and structured into a comprehensive database. Descriptive statistics, chi-square tests, logistic and Poisson regressions, and interaction analyses were performed to identify patterns in victim de-mographics, breed involvement, ownership status, and environmental context. A total of 54 fatal attacks were recorded, with a rising trend in the last five years. Elderly individuals (≥65) and preschool-aged children (≤4) were the most affected groups. Molosser and bull-type breeds were implicated in 69% of cases, and 92.6% of attacks in-volved owned dogs—more than half by dogs belonging to the victim. Private settings accounted for 66.7% of incidents. Comparative analysis with U.S. data revealed similar demographic and breed-related patterns, but highlighted Italy’s lack of a centralized behavioral risk registry. Fatal dog attacks in Italy follow recurring and preventable patterns. The absence of a na-tional database severely limits surveillance and intervention. A centralized behavioral risk registry—modeled on international systems—should be established to support early detection, policy development, and multidisciplinary collaboration.