Due to nanomaterials? potential benefits for diagnosis and treatment, they are widely used in medical applications and personal care products. Interaction of nanomaterials, which are very small in size, with tissue, cell and microenvironment, can reveal harmful effects that cannot be created with chemically identical and larger counterparts in biological organisms. In this review, a challenge for future medicine, nanotoxicity of nanomaterials is discussed. A detailed review of related literature was performed and evaluated as per medical applications of nanomaterials their toxicity. Most authors state ?the only valid technology will be nanotechnology in the next era?; however, there is no consensus on the impact of this technology on humankind, environment and ecological balance. Studies dealing with the toxic effect of nanomaterials on human health have also varied with developing technology. Nanotoxicology studies such as in-vivo-like on 3D human organs, cells, advanced genetic studies, and -omic approaches begin to replace conventional methods. Nanotoxicity and adverse effects of nanomaterials in exposed producers, industry workers, and patients make nanomaterials a double-edged sword for future medicine. In order to control and tackle related risks, regulation and legislations should be implemented, and researchers have to conduct joint multidisciplinary studies in various fields of medical sciences, nanotechnology, nanomedicine, and biomedical engineering.

Forensic medicine and sciences is a multidisciplinary branch of science, which frequently benefit from novel technologies. State-of-the-art omics technologies have begun to be performed in forensic medicine and sciences, particularly in post-mortem interval, intoxication, drugs of abuse, diagnosis of diseases and cause of death. This review aims to discuss the role and use of great omics -metabolomics, proteomics, genomics and transcriptomics- in forensic sciences, in detail. A detailed review of related literature was performed, and studies were subdivided as per the type of omics. Omics seems as a revolutionary step in forensic science and sure carries it towards a new age. The number of forensic studies utilizing omics steadily increases in last years. Omics strategies should be used together in order to gather more accurate and certain data. Additional studies need to be performed to incorporate omics into routine forensic methodology.

Background: The rise of artificial intelligence in the field of medicine has had a wide-ranging impact, from clinical applications to research. Artificial intelligence-based language models such as Chat Generative Pre-Trained Transformer have the potential to expedite the process of forensic reporting, particularly in forensic medicine. This study aims to evaluate the forensic reporting capabilities of Chat Generative Pre-Trained Transformer-4 in comparison with forensic medicine assistants. The Turkish Penal Code-related forensic medicine guide and 20 case examples were used to train Chat Generative Pre-Trained Transformer-4 in this study. Chat Generative Pre-Trained Transformer-4 was asked to write reports like forensic medicine specialists. In the retrospective phase, 100 forensic cases were assessed by Chat Generative Pre-Trained Transformer-4, while in the prospective phase, 266 new cases were assessed by both Chat Generative Pre-Trained Transformer-4 and forensic medicine assistants. Two forensic medicine specialists assessed the accuracy of these reports in terms of adherence to the forensic medicine guide. Results: Chat Generative Pre-Trained Transformer-4 achieved an accuracy rate of 96.6% in the retrospective phase and 96.2% in the prospective phase for the combined categories of “Life-threatening” and “Simple Medical Intervention”. Forensic medicine assistants, however, demonstrated a higher accuracy rate of 99.1% in these categories compared to Chat Generative Pre-Trained Transformer-4. Conclusions: The success of Chat Generative Pre-Trained Transformer-4 indicates that the combination of technology and human expertise could establish new standards in forensic reporting. However, it is emphasized that supervision by forensic medicine specialists remains crucial in this process.

Objective: This study presents a structured evaluation of large language models (LLMs) in predicting suicide methods based exclusively on indirect forensic psychiatric indicators. Methods: Ninety-two forensic psychiatric cases (2019–2024), involving survivors of suicide attempts formally examined in medico-legal contexts, were retrospectively analyzed. Variables included age, sex, psychiatric diagnosis, previous suicide attempts, psychiatric medication use, impulsivity, and consciousness at emergency admission. Six LLMs were tested: ChatGPT-4o, ChatGPT-4o Mini, ChatGPT-O3 (OpenAI), Gemini 2.0 Flash, Gemini 2.5 Pro, and Gemini 2.5 Flash (Google DeepMind). Each case was converted into a standardized anonymized prompt. Model predictions were categorized by blinded forensic physicians and evaluated using accuracy, precision, recall, F1-score, and Cohen’s Kappa for 1-month reproducibility. Results: Gemini 2.5 Flash achieved the highest performance with 76.09% accuracy, 46.9% F1-score, and 45.2% recall. It accurately predicted the dominant method, medication overdose, but underperformed for rare categories. Temporal reproducibility was moderate (κ = 0.582), while other models exhibited lower and less stable performance. Conclusion: LLMs can infer suicide methods from indirect psychiatric data with encouraging accuracy. However, limitations in detecting rare methods and maintaining temporal consistency suggest the need for further methodological refinement and external validation prior to forensic application.

Background: As in all over the world, healthcare professionals in Turkey are increasingly affected by violence. The purpose of this study was to evaluate homicide, which is the most severe form of violence against health care professionals.Methods: Information regarding characteristics of the incidents, victims, and perpetrators were gathered from the media reports subjecting murders of healthcare professionals between 2000 and 2019 were compiled and discussed with descriptive statistics.Results: There were 60 homicides involving health professionals (35 male, 25 female) between 2000 and 2019. Homicides of health professionals were found to have increased significantly between 2015 and 2019. The most common crime scene was the victim’s workplace, whereas the most common method of homicide was the use of firearms. In female healthcare professional homicides, the murderer was a former or existing partner in most cases.Conclusions: The assessment of cultural factors, and the performance of various studies to prevent homicide of health professionals is crucial. Policies against individual weaponry and the prevention of armed persons entering healthcare facilities could be useful. In addition, regulations prohibiting targeting health professionals as a scapegoat for system failures will help address this problem.