Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients’ outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. This article explores the diverse applications and reviews the current state of AI adoption in healthcare. It concludes by emphasizing the need for collaboration between physicians and technology experts to harness the full potential of AI.

Artificial intelligence (AI) is a reality of our times, and it has been successfully implemented in all fields, including medicine. As a relatively new domain, all efforts are directed towards creating algorithms applicable in most medical specialties. Pathology, as one of the most important areas of interest for precision medicine, has received significant attention in the development and implementation of AI algorithms. This focus is especially important for achieving accurate diagnoses. Moreover, immunohistochemistry (IHC) serves as a complementary diagnostic tool in pathology. It can be further augmented through the application of deep learning (DL) and machine learning (ML) algorithms for assessing and analyzing immunohistochemical markers. Such advancements can aid in delineating targeted therapeutic approaches and prognostic stratification. This article explores the applications and integration of various AI software programs and platforms used in immunohistochemical analysis. It concludes by highlighting the application of these technologies to pathologies such as breast, prostate, lung, melanocytic proliferations, and hematologic conditions. Additionally, it underscores the necessity for further innovative diagnostic algorithms to assist physicians in the diagnostic process.

Background: Mortality is often used as an indicator of public health efforts. Even if mortality in psychiatric hospitals decreased since the introduction of modern treatment, the death toll is still high. The authors have analyzed the forensic autopsy data and the medical documentation regarding 115 death cases from psychiatric hospitals in south-eastern Romania during the period of 2000–2020. Results: The average annual mortality rate was 5.13‰, the necropsy data corroborated with those from the medical documentary material indicates acute myocardial infarction as the dominant cause, with 65 (56.5%) cases, followed by upper respiratory tract occlusion with 23 cases (20%) and pulmonary thromboembolism in 4 cases (12.2%). Furthermore, in 6 cases (5.2%) the cause of death was traumatic: 4 cases of cranio-cerebral trauma and 2 cases of hanging. Conclusions: In the mortality structure of psychiatric patients, cardiac death predominated, being influenced by the cardiotoxic effect of medication administered for the specific pathology; hence, an early involvement of cardiologists in the follow-up of patients and the finding of treatment schemes with a reduced cardiotoxic effect are required.

Spontaneous rupture of the urinary bladder is a rare clinical condition, with a prevalence of about 1 in 126.000 hospitalizations. Idiopathic rupture is even more rarely encountered, constituting less than 1% of all cases. Given the low incidence, atypical symptoms and poor history data, the diagnosis is often established late and treatment is delayed, resulting in a high rate of mortality. Also, spontaneous rupture of the urinary bladder may be a surprise at the autopsy. In this paper, the authors present the case of a 67 years old woman who died due to idiopathic spontaneous rupture of the urinary bladder after ingestion of alcoholic beverages. The autopsy showed no signs of pathological or traumatic injury and the toxicological examination revealed a blood alcohol concentration of 2.05 g ‰. The case is analyzed in the context of existing literature data, taking into account the elements of anatomy, physiology and pathophysiology that can determine or favour the idiopathic spontaneous rupture of the urinary bladder.

Background: In recent decades, machine-learning (ML) technologies have advanced the management of high-dimensional and complex cancer data by developing reliable and user-friendly automated diagnostic tools for clinical applications. Immunohistochemistry (IHC) is an essential staining method that enables the identification of cellular origins by analyzing the expression of specific antigens within tissue samples. The aim of this study was to identify a model that could predict histopathological diagnoses based on specific immunohistochemical markers. Methods: The XGBoost learning model was applied, where the input variable (target variable) was the histopathological diagnosis and the predictors (independent variables influencing the target variable) were the immunohistochemical markers. Results: Our study demonstrated a precision rate of 85.97% within the dataset, indicating a high level of performance and suggesting that the model is generally reliable in producing accurate predictions. Conclusions: This study demonstrated the feasibility and clinical efficacy of utilizing the probabilistic decision tree algorithm to differentiate tumor diagnoses according to immunohistochemistry profiles.