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Приспособление к искусственным нейронным сетям и использование искусственного интеллекта в качестве инструмента диагностики туберкулеза: обзор.

Туберкулез (ТБ) затронул многие страны мира. Согласно докладу Всемирной организации здравоохранения (ВОЗ), в 2015 году было зарегистрировано 1,4 млн. случаев смерти от туберкулеза и еще 0,4 млн. случаев смерти от туберкулеза среди лиц, живущих с ВИЧ. Большинство случаев смерти от туберкулеза можно предотвратить, если они обнаруживаются на ранней стадии. Существующие процессы диагностики, такие, как анализ крови или анализ слюны, не только утомительны, но и требуют длительного времени для анализа и не могут проводить различие между различными стадиями туберкулеза, устойчивыми к лекарственным пр

Совпадение в аннотации оригинала
Tuberculosis [TB] has afflicted numerous nations in the world. As per a report by the World Health Organization [WHO], an estimated 1.4 mill … ges of TB. The need to find newer prompt methods for disease detection has been aided by the latest Artificial Intelligence [AI] tools. Artificial Neural Network [ANN] is one of the important tools that is being used widely in diagnosis and evaluation of medical conditions. This review aims at providing brief introduction to various AI tools that are used in TB detection …

The Use of Artificial Intelligence in Idiopathic Clubfoot: A Scoping Review.

BACKGROUND: The past decade has been marked by the progressive integration of artificial intelligence (AI), especially in the form of machine learning and deep learning. Pediatric orthopaedics remains an underexplored area in AI, especially in the context of congenital deformities such as clubfoot. OBJECTIVE: A scoping review would help organize the various uses of AI in this domain by identifying studies and categorizing them based on the technique used and their applications, exposing gaps, and informing future studies to support AI integration in pediatric care. METHODS: A Preferred Repor…

The Role of Artificial Intelligence in Oral Dysplasia and Oral Cancer.

Oral epithelial dysplasia carries a variable risk of progression to oral squamous cell carcinoma, and conventional histopathologic grading cannot reliably predict the behavior of an individual lesion. Artificial intelligence offers a means of extracting additional information from data already obtained in routine care, including histologic images, and additional information regarding biomarkers, the tumor immune microenvironment, and genetic or epigenetic profiles. Machine learning and deep learning approaches are being investigated to estimate malignant transformation risk in dysplasia and to…

A Peircean framework for explainable and human-centred artificial intelligence.

This article develops a philosophical-conceptual framework for rethinking Explainable Artificial Intelligence (XAI) and Human-Centred Artificial Intelligence (HCAI) through the pragmatic semiotics of Charles Sanders Peirce. Rather than treating explainability solely as algorithmic transparency, the article conceptualizes it as a socially situated epistemic process through which AI-generated outputs are interpreted, evaluated, contested, and revised within human practices of inquiry. Drawing on Peirce's concepts of semiosis, abduction, inquiry, fallibilism, pragmaticism, and the community of in…

The role of artificial intelligence in precision medicine for breast cancer.

Breast cancer (BC) ranks among the most common malignant tumors affecting women globally. The essence of precision medicine (PM) lies in "delivering the right treatment to the right patient at the right time." With advancements in artificial intelligence (AI) technologies such as deep learning (DL), breakthroughs have been achieved in analyzing data ranging from imaging to multi-omics. We review the latest applications and challenges of AI in PM for BC, offering insights for clinical practice and research. We also present an AI integration framework covering the entire BC care continuum. The f…

Ethical Considerations for the Use of Artificial Intelligence Tools in Surgery.

Artificial intelligence (AI) is rapidly transforming surgical care, offering unprecedented capabilities in diagnostics, planning, and intraoperative guidance. However, its integration into clinical practice raises complex ethical challenges that must be addressed to ensure responsible and equitable use. This chapter aims to explore the ethical implications of AI in surgery through the lens of the four foundational principles of medical ethics: autonomy, beneficence, nonmaleficence, and justice. Respecting patient autonomy requires clinicians to disclose all relevant information and ensure unde…

The landscape of artificial intelligence in neurodegenerative diseases: a systematic review.

BACKGROUND: The rising global burden of neurodegenerative diseases underscores an urgent need for advanced research in diagnosis, prognosis, and treatment. Artificial Intelligence (AI) methods, particularly when applied to multimodal data, offer a powerful tool to address these challenges. However, a comprehensive overview and critique of the current landscape of AI methods is lacking. METHODS: 4,685 records of peer-reviewed, primary research articles were screened and 1,956 articles reviewed in full text, yielding 1,186 included studies. For each included study, clinical objectives, disease …

[Application of artificial intelligence in pathological diagnosis of ocular tumors].

Ocular tumors encompass ocular surface tumors, orbital tumors, and intraocular tumors, characterized by high heterogeneity and complex classifications, which pose considerable challenges to pathological diagnosis. The scarcity of specialized ophthalmic pathologists further exacerbates the difficulty in diagnosing these tumors. In recent years, artificial intelligence (AI) has penetrated diverse areas of healthcare, particularly in departments with intensive medical imaging and image-based workflows, such as radiology, pathology, and ultrasonography. With the advent of the digital pathology era…

The impact of artificial intelligence on periodontal disease detection and treatment.

Periodontal disease (PD) is one of the most prevalent chronic inflammatory non-comunicable diseases worldwide. Early diagnosis and timely intervention for periodontitis are essential to prevent the onset and progression of the disease, especially due to the associated risk, sometimes subclinical, of negative correlations with various systemic diseases that impair quality of life. In this regard, artificial intelligence (AI) has emerged as a transformative tool in healthcare, and its application in the detection and treatment of periodontal disease holds considerable promise. This study aims to…

Use of Artificial Intelligence in the Interpretation of Electroretinography (ERG) Studies.

Electroretinograms are an important diagnostic tool to measure retinal electrical activity. However, their interpretation, done by sub-specialised ophthalmologists, can be not only time consuming but also challenging to obtain due to availability. In recent years, studies have investigated the use of artificial intelligence in the analysis of electroretinograms. This systematic review summarises the accuracy of artificial intelligence in interpreting electroretinograms and appraises the studies included. The review comprises primary, peer-reviewed published studies that determined accuracy of …

Application of artificial intelligence in gynecologic cancers: A bibliometric analysis.

OBJECTIVES: This study aimed to systematically characterize the landscape of artificial intelligence (AI) applications in gynecologic cancers, offering a comprehensive overview of current research trends, influential publications, key contributors, and future research directions. The focus of this study was to provide a quantitative overview of the field's development and trends. MATERIALS AND METHODS: A structured search was performed in the Web of Science Core Collection to identify original articles on AI use in gynecologic oncology. Two independent reviewers screened and selected studies …

A systematic review of economic evidence of artificial intelligence in healthcare.

BACKGROUND: Ambitious claims suggest AI could save $200-360 billion annually in US healthcare and €212 billion in Europe, though the empirical evidence base supporting these projections remains unclear. OBJECTIVE: This systematic review seeks to synthesise the available economic evidence of AI technologies in healthcare and contextualise the available economic evidence against the broader policy expectations surrounding the economic impact of AI in healthcare. METHODS: We searched MEDLINE, Embase, Global Health, PsycINFO, and Cochrane Central for scientific sources. The JBI dominance ranking

Assessment of artificial intelligence in detecting errors on panoramic radiographs.

PURPOSE: This study aimed to evaluate the ability of artificial intelligence (AI) to detect common errors in panoramic radiographs. MATERIALS AND METHODS: This retrospective study utilized a dataset of 2,888 anonymized panoramic radiographs obtained from multiple dental imaging units. Three annotators classified the images into 3 categories: "uneven magnification," "tongue space," and "normal." Three deep learning architectures-Attention Dental X-ray, ResNet50, and MobileNet-were developed and evaluated. Model performance was assessed using accuracy, precision, sensitivity, area under the cur…

The fusion of artificial intelligence and omics: a perspective toward 2035.

Artificial intelligence (AI) has catalyzed a revolution in the life sciences, culminating in AlphaFold's capacity to predict the static structure of proteins with atomic precision. However, this static view-analogous to a molecular radiograph-omits the fundamental dimension of protein dynamics, a critical factor for biological function and drug interaction. The phenomenon of induced fit, whereby proteins reconfigure upon ligand binding, remains largely beyond the reach of current models. This article posits that the next frontier in computational biology lies in the adoption of World Models-AI…

Artificial Intelligence in Cardiology: Applications in Diagnosis and Risk Prediction.

Cardiovascular diseases (CVDs) continue to be a major cause of death and morbidity throughout the world, and there is an increasing need for better diagnostic and predictive approaches. Existing methods do not fully reflect complex clinical interactions, and new computational methods possess greater capabilities. The limitations, including a lack of diversity in datasets, low generalizability, and limited interpretability, limit general use in the clinic. The review highlights the latest developments in artificial intelligence (AI) for diagnosing and predicting cardiovascular risk factors, wit…

Development of a hybrid artificial intelligence framework for pharmacotherapy optimization.

INTRODUCTION: Pharmacotherapy optimization in multimorbid patients is increasingly complex due to polypharmacy, fragmented data, expanding electronic health records, and workforce constraints. Conventional clinical decision support systems remain largely rule-based and often fail to adequately incorporate patient-specific context. While artificial intelligence offers new opportunities, stand-alone models remain insufficiently reliable for high-risk pharmacotherapy decision support. AIM: To develop a relevance-driven, clinician-supervised hybrid AI framework for pharmacotherapy optimization. …