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The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review.

Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative approaches in antimicrobial stewardship (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in this domain, enabling data-driven interventions to optimize antibiotic use and combat resistance. This comprehensive review explores the multifaceted role of AI and ML models in enhancing antimicrobial stewardship efforts across healthcare systems. AI-powered predictive analytics can identify patterns of resistance, forecast outbreaks, and guide personalized an…

Ambient Artificial Intelligence Scribes: A Pilot Survey of Perspectives on the Utility and Documentation Burden in Palliative Medicine.

BACKGROUND/OBJECTIVES: There is growing evidence to support ambient artificial intelligence (AI) scribes in healthcare to improve medical documentation by generating timely and comprehensive notes. Using the Plan-Do-Study-Act (PDSA) methodology, this study evaluated the utility and potential time savings of an ambient AI scribe, Scribeberry, (V2), in a palliative medicine outpatient setting, comparing it to the standard practice of dictation. METHODS: This prospective quality improvement study was conducted at an academic medical center by two palliative medicine resident physicians. Resident…

The Impact of Artificial Intelligence Technologies on Nutritional Care in Patients With Chronic Kidney Disease: A Systematic Review.

OBJECTIVES: Chronic kidney disease is a global health challenge, and effective, individualized nutritional management is crucial for slowing progression and improving quality of life. Artificial intelligence (AI) offers innovative tools to optimize and personalize nutritional care. This review explores AI applications in nutritional management, assessing their impact on clinical outcomes, quality of life, and care efficiency. METHODS: A systematic review was conducted, reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Searches were perfor…

The Emerging Clinical Relevance of Artificial Intelligence, Data Science, and Wearable Devices in Headache: A Narrative Review.

This narrative review introduces key concepts in artificial intelligence (AI), data science, and wearable devices aimed at headache clinicians and researchers. PubMed and IEEEXplore were searched to identify relevant studies, and these were reviewed systematically. We identified six primary research topics. First, the most common application of AI and data science is in the diagnosis of headache disorders, with reported accuracies of up to 90%. Second, AI and data science are used for predicting headache disease trajectories and forecasting attacks. Third, prediction of treatment effects and d…

Introducing artificial intelligence and sperm epigenetics in the fertility clinic: a novel foundation for diagnostics and prediction modelling.

Worldwide, infertility is a rising problem. A couple's lifestyle, age and environmental exposures can interfere with reproductive health. The scientific field tries to understand the various processes how male and female factors may affect fertility, but translation to the clinic is limited. I here emphasize potential reasons for failure in optimal treatment planning and especially why current prediction modelling falls short. First, Assisted Reproductive Technology (ART) has become a mainstream solution for couples experiencing infertility, while potential causes of infertility remain unexplo

Artificial Intelligence in the Management and Treatment of Pediatric Burns: A Narrative Review of Current Applications and Future Directions.

Pediatric burn evaluation is often subjective; early estimates of depth and TBSA% drive triage, surgery, and follow-up. Emerging artificial intelligence (AI) tools, especially image-based methods, aim to standardize assessment and extend expertise via telemedicine. To narratively review AI applications relevant to pediatric burn care across diagnosis (depth/healing), TBSA% estimation, treatment planning/monitoring, telemedicine, and rehabilitation, and to summarize practical and ethical considerations for translation. Narrative review of PubMed/MEDLINE and Embase (January 2000-June 2025) with …

Artificial Intelligence for Predicting Postoperative Complications in Orthopedics: A Review of Clinical Applications, Challenges, and Future Directions.

Postoperative complications, including infections, venous thromboembolism (VTE), and prolonged length of stay (LOS), remain major sources of morbidity and healthcare expenditure in orthopedic surgery. While traditional risk stratification tools provide useful benchmarks, they often fall short in delivering precise, individualized predictions. This review extends prior work by providing a direct comparative synthesis of artificial intelligence (AI) and traditional statistical models in orthopedics, while proposing a roadmap of the Validation, Integration, and Regulation (VIR) framework for resp…

Study of the L-Carnitine Effect on Myocardial Bioelectrical Activity by Microelectrode Arrays and Artificial Intelligence Application.

The article presents the results of applying artificial intelligence algorithms to analyze multichannel electrograms of bioelectrical activity registered by microelectrode arrays from myocardium of isolated rat hearts under the influence of L-carnitine and adrenaline. Localization of activation times on electrograms was performed using a new neural network model based on the U-Net architecture adapted for one-dimensional signal segmentation. In the organism, L-carnitine is necessary for the oxidation of fatty acids. But the effects of its supplemental intake on the cardiovascular system are st…

Global Disparities and Artificial Intelligence in Plastic Surgery: A Narrative Review of Current Applications and Ethical Implications.

Global disparities in surgical access remain a major health challenge, with an estimated 5 billion people lacking safe and affordable care. … local capacity, yet workforce shortages and infrastructure limitations remain significant barriers. Artificial intelligence (AI) offers a potential avenue for innovation. In high-income countries, AI has been applied to preoperative planning, postoperative monitoring, patient education, and workforce training. These tools could benefit LMICs by reducing reliance on perso …

External validation of an artificial intelligence tool for fracture detection in children with osteogenesis imperfecta: a multireader study.

OBJECTIVE: To determine the performance of a commercially available AI tool for fracture detection when used in children with osteogenesis i … nslate into improved patient outcomes. KEY POINTS: Question How well does a commercially available artificial intelligence (AI) tool identify fractures, on appendicular radiographs of children with osteogenesis imperfecta (OI), and can it also improve radiologists' identification of fractures in this population? Findings Specialist human radiologists outperformed the AI …

The application of artificial intelligence in the acute and sub-acute phases of spinal cord injury- a systematic review.

STUDY DESIGN: Systematic Review. OBJECTIVE: To describe applications of AI for traumatic SCI management with focus on diagnostics, prognostication, and therapeutic interventions. METHODS: PubMed, Scopus and Cochrane libraries were searched (March 2025). Studies published in English between January 1st, 2020, and March 18, 2025, dealing with clinical aspects in the acute, post-injury rehabilitative and first year phases of SCI were included. Studies on brain computer interface, robotics and non-neurologic aspects of SCI were excluded. Extracted were country of study, study design, focus of st

Sarcopenia measured by artificial intelligence as a predictor of overall survival in localized bladder cancer, a multicenter study.

BACKGROUND: Sarcopenia, characterized by a loss of skeletal muscle mass and function, is a poor prognosis risk factor in various cancers. Diagnosis typically involves quantifying skeletal muscle area at the L3 vertebral level (SMA) using CT imaging, however a universally accepted definition of sarcopenia does not exist. METHODS: In a retrospective, multicenter study, we analyzed data from 87 muscle-invasive bladder cancer patients who received neoadjuvant chemotherapy followed by radical cystectomy. Artificial intelligence (AI) was used to evaluate CT scans obtained before chemotherapy (BC) a…

Current and Emerging Applications of Artificial Intelligence in Medical Imaging for Paediatric Hip Disorders-A Scoping Review.

INTRODUCTION: Paediatric hip disorders present unique challenges for artificial intelligence (AI)-aided assessments of medical imaging due to disease-related and age-dependent changes in hip morphology. This scoping review aimed to describe current and emerging applications of AI in medical imaging for paediatric hip disorders. METHODS: A descriptive synthesis of articles identified through PubMed, Embase, Cochrane Library, Web of Science, Emcare, and Academic Search Premier databases was performed including articles published up until June 2024. Original research articles' titles and abstrac…

Diagnostic Accuracy of Artificial Intelligence for the Detection of Papilledema on Fundus Images: A Systematic Review and Meta-Analysis.

Papilledema, a vision-threatening, raised intracranial pressure manifestation, must be recognized quickly to avoid permanent optic nerve injury. AI techniques and deep learning models, while promising for automating fundus-based detection of papilledema, have shown variable diagnostic accuracies across studies because of varied datasets, differences in grading criteria, and validation methodologies. A systematic review and meta-analysis was performed according to PRISMA-DTA (Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies) 2020 guidelines

A novel approach for classifying patients with adrenal tumors based on decision support systems and artificial intelligence.

AIMS: Adrenal incidentalomas (AIs) encompass a wide range of clinical entities, from incidental benign neoplasms that need to be monitored to aggressive malignancies requiring urgent medical intervention and treatment. The incidence of adrenal tumors is steadily rising, reflecting a growing trend in their prevalence and highlighting the necessity for heightened awareness and advanced diagnostic strategies to address this escalating health concern. This retrospective study was undertaken in order to explore the possibility of developing a decision support system for classifying adrenal tumors a

Sex disparities in the phenotype at diagnosis of Sjögren's disease: artificial intelligence-driven characterisation in 17,416 patients.

OBJECTIVES: Sjögren disease (SjD) predominantly affects females, but the early disease presentation in male patients remains poorly characterised due to historically small sample sizes. The aim of this study was to investigate sex‑based differences in the clinical phenotype at diagnosis of SjD and identify predictors of patient sex using a large international cohort and AI‑enhanced analysis. METHODS: Cross-sectional analysis of an anonymised dataset comprising 17,416 worldwide patients fulfilling the 2002/2016 classification criteria (Sjögren Big Data Registry). We stratified the dataset by s

Capillariid diversity in archaeological material from the New and the Old World: clustering and artificial intelligence approaches.

BACKGROUND: Capillariid nematode eggs have been reported in archaeological material in both the New and the Old World, mainly in Europe and … roaches to species identification were applied: discriminant analysis, hierarchical clustering, and artificial intelligence/machine learning. RESULTS: A total of 10 samples from Europe and 4 from Brazil were positive for capillariid eggs, showing 13 different morphotypes. As European samples were mainly collected from latrines and pits, parasite-host information was abs …

Applications of Artificial Intelligence as a Prognostic Tool in the Management of Acute Aortic Syndrome and Aneurysm: A Comprehensive Review.

Acute Aortic Syndromes (AAS) and Thoracic Aortic Aneurysm (TAA) remain among the most fatal cardiovascular emergencies, with mortality rising by the hour if diagnosis and treatment are delayed. Despite advances in imaging and surgical techniques, current clinical decision-making still relies heavily on population-based parameters such as maximum aortic diameter, which fail to capture the biological and biomechanical complexity underlying these conditions. In today's data-rich era, where vast clinical, imaging, and biomarker datasets are available, artificial intelligence (AI) has emerged as a …

The Role of Artificial Intelligence, Including Endoscopic Diagnosis, in the Prediction of Presence, Bleeding, and Mortality of Esophageal Varices.

Esophagogastric varices (EGVs) are a disease that occurs as a complication of the progression of liver cirrhosis, and since bleeding can be fatal, regular endoscopy is necessary. With the development of artificial intelligence (AI) in recent years, it is beginning to be applied to predicting the presence of EGVs, predicting bleeding, and making a diagnosis and prognosis. Based on previous reports, application methods of AI can be classified into the following four categories: (1) noninvasive prediction using clinical data obtained from clinical records such as laboratory data, past history, an…

Текстурный анализ и искусственный интеллект как инструменты поддержки решений в диагностике множественного склероза - систематический обзор.

ВВЕДЕНИЕ: Магнитная резонансная визуализация (MRI) обычно используется для обнаружения и диагностики множественных склерозов (MS), часто дополняемых поясничными проколами - очень инвазивным методом для подтверждения диагноза. Кроме того, МРТ периодически повторяется для контроля за развитием и эффективностью лечения заболеваний. В ходе недавних исследований основное внимание уделялось применению искусственного интеллекта (АИ) и радиомики при обработке медицинских изображений, диагностике и планировании лечения. МЕТОДЫ: Был проведен обзор существующей литературы с анализом использования моделей

Совпадение в аннотации оригинала
INTRODUCTION: Magnetic resonance imaging (MRI) is conventionally used for the detection and diagnosis of multiple sclerosis (MS), often comp … nitor disease progression and treatment efficacy. Recent research has focused on the application of artificial intelligence (AI) and radiomics in medical image processing, diagnosis, and treatment planning. METHODS: A review of the current literature was conducted, analyzing the use of AI models and texture analysis for MS lesion segmentation and classification. The stu …