Background: Ideas about Artificial intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided are becoming a bigger pool and we need artificial intelligence that will help us with its superhuman powers. Its interaction with medicine is improving more and more, with medicine being a domain that continues to be perfected. Materials and Methods: The most important databases were used to perform this detailed search that addresses artificial intelligence in the medical and surgical fields. Discussion: Machine learning, deep learning, neural networks and computer vision are some of the mechanisms that are becoming a trend in healthcare worldwide. Developed countries such as Japan, France and Germany have already implemented artificial intelligence in their medical systems. The help it gives is in medical diagnosis, patient monitoring, personalized therapy and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques and to choose the best surgical techniques. Conclusions: The goal is to predict complications, reduce diagnostic times, diagnose complex pathologies, guide surgeons intraoperatively and reduce medical errors. We are at the beginning of this, and the potential is enormous, but we must not forget the impediments that may appear and slow down its implementation.

Background/Objectives: Sepsis is a severe medical condition characterized by a dysregulated host response to infection, with potentially fatal outcomes, requiring early diagnosis and rapid intervention. The limitations of traditional sepsis identification methods, as well as the complexity of clinical data generated in intensive care, have driven increased interest in applying artificial intelligence in this field. The aim of this narrative review article is to analyze how artificial intelligence is being used in the diagnosis and prognosis of sepsis, to present the most relevant current models and algorithms, and to discuss the challenges and opportunities related to integrating these technologies into clinical practice. Methods: We conducted a structured literature search for this narrative review, covering studies published between 2016 and 2024 in databases such as PubMed/Medline, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The review covered models based on machine learning (ML), deep neural networks (DNNs), Recurrent Neural Networks (RNNs), and clinical alert systems implemented in hospitals. The clinical data sources used, algorithms applied, system architectures, and performance outcomes are presented. Results: Numerous artificial intelligence models demonstrated superior performance compared to conventional clinical scores (qSOFA, SIRS), achieving AUC values above 0.90 in predicting sepsis and mortality. Systems such as Targeted Real-Time Early Warning System (TREWS) and InSight have been clinically validated and have significantly reduced the time to treatment initiation. However, challenges remain, such as a lack of model transparency, algorithmic bias, difficulties integrating into clinical workflows, and the absence of external validation in multicenter settings. Conclusions: Artificial intelligence has the potential to transform sepsis management through early diagnosis, risk stratification, and personalized treatment. A responsible, multidisciplinary approach is necessary, including rigorous clinical validation, enhanced interpretability, and training of healthcare personnel to effectively integrate these technologies into everyday practice.

Generalized secondary peritonitis is a life-threatening intra-abdominal infection requiring urgent surgical intervention. Despite advances in surgical and antimicrobial therapy, morbidity and mortality remain high. Identifying key prognostic factors is crucial for improving patient outcomes. This review examines significant prognostic indicators and explores the potential role of scoring systems and artificial intelligence in risk stratification. A review was conducted using PubMed, Web of Science, Scopus, and Medline databases. Studies published from 2000 to 2024 focusing on prognostic factors in secondary peritonitis were included. A total of 145 studies were identified, with 40 selected based on relevance and methodological quality. Data extraction included patient demographics, comorbidities, severity scores, microbiological profiles, and artificial intelligence applications in peritonitis management. Poor prognosis was associated with advanced age, severe sepsis, organ failure, chronic kidney disease, cardiovascular comorbidities, and diabetes mellitus. The Mannheim Peritonitis Index (MPI) remains a widely validated prognostic tool, while APACHE II and SOFA scores also provide valuable risk estimates. Increasing multidrug-resistant infections further complicate management and impact outcomes. Emerging evidence suggests that machine learning algorithms may improve early risk stratification and individualized outcome prediction when integrated with conventional scoring systems. Identifying prognostic factors remains essential for optimizing outcomes in secondary peritonitis, and future research should prioritize the clinical validation and integration of AI-based models into perioperative management protocols.

Ideas about Artificial Intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided is getting bigger and bigger and, in this case, we need artificial intelligence that helps us with superhuman powers. The interaction with medicine is getting closer and closer, medicine being a field that continues to be perfected. Machine learning, deep learning, neural networks, computer vision are some of the mechanisms that are already applied in medicine, becoming a trend in healthcare worldwide. Artificial intelligence will be the thing that will change contemporary medicine. Developed countries such as Japan, France, Germany have already implemented artificial intelligence in their medical systems. The help is in medical diagnosis, patient monitoring, personalized therapy, and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques, to choose the best surgical technique. The goal is to predict complications, reduce diagnostic time, diagnose complex pathologies, guide the surgeon intraoperatively and reduce medical errors. We are at the beginning but the potential is enormous.

Breast cancer is the most prevalent form of malignancy among women worldwide. Breast cancer can emerge on the skin in two ways: directly, as skin metastases or a direct extension of the tumor; or indirectly, as a paraneoplastic disease. Breast cancer imaging is an essential part of breast cancer screening, diagnosis, preoperative/treatment assessment, and follow-up. The selection of optimal imaging modalities should be contingent upon the patient’s risk factors and breast density since breast cancer imaging necessitates a personalized approach rather than a universal solution. There are a lot of different skin conditions that point to breast cancer, either as a sign of recurring disease, undiagnosed breast cancer, or a condition that puts you at a higher risk of getting it. Even though reconstruction has been shown to improve quality of life for many women, there are still a lot of complicated factors that affect morbidity and results.

Introduction: Acute secondary peritonitis is a common clinical condition encountered in emergency hospital units, affecting a wide range of patients. The mortality rate for these patients is high and increases with the onset of sepsis, while the risk factors associated with poor outcomes remain under debate. The aim of this study was to analyze the prognostic factors that increase morbidity and mortality in generalized secondary peritonitis. Generalized secondary peritonitis accounts for approximately 1% of emergency hospital admissions. Mortality in generalized secondary peritonitis remains high, ranging from 6% to as much as 55% in cases of severe sepsis, according to various studies. Materials and methods: This study was conducted by searching publications listed on the most prominent search engines and identifying the most relevant articles from our perspective, with a focus on prioritizing studies from recent years. Results/Discussion: Multiple prognostic factors were identified in the studied articles, including age, the presence of associated conditions (cardiovascular, renal, hepatic, neurological, metabolic, oncological), preoperative time, type of peritonitis, septic shock, or severe sepsis. Conclusions: The most consistent prognostic factors increasing morbidity and mortality were advanced age, cardiovascular diseases, chronic renal failure, cancer, and sepsis. Scoring systems greatly assist us in daily practice, and the simpler they are, the more practical they become. Artificial intelligence is making its way into all fields, promising to revolutionize the monitoring of patients with peritonitis.