Результаты поиска

[Differentiation of osteoarthritis phenotypes on MRI using artificial intelligence].

BACKGROUND: Osteoarthritis (OA) is one of the most common joint diseases, affecting more than 500 million people worldwide. In recent decades, there has only been limited progress in terms of diagnosis and treatment. For a long time, OA was considered to be primarily a mechanically induced degenerative disease. However, more recent work has shown that OA is a heterogeneous condition that manifests in different phenotypes. Although artificial intelligence (AI) is becoming increasingly important in medical research, its specific application in the field of OA remains limited in clinical use. OB…

Artificial intelligence-driven transformative applications in disease diagnosis technology.

The integration of artificial intelligence (AI) in medical diagnostics represents a transformative advancement in healthcare, with projected market growth reaching $188 billion by 2030. This comprehensive review examines the latest developments in AI-driven diagnostic technologies across multiple disease domains, particularly focusing on cancer, Alzheimer's disease (AD), and diabetes. Through systematic bibliometric analysis using GraphRAG methodology, we analyzed research publications from 2022 to 2024, revealing the distribution and impact of AI applications across various medical fields. In…

[Line-field confocal optical coherence tomography and artificial intelligence].

BACKGROUND: The diagnosis of actinic keratosis (AK), basal cell carcinoma (BCC), and psoriasis may present a challenge in everyday dermatological practice. Clinical and dermoscopic assessments often reach their limits, especially in ambiguous or anatomically difficult-to-access lesions. Biopsies are often impractical, and objective tools for treatment monitoring are lacking. OBJECTIVE: To investigate the potential of line-field confocal optical coherence tomography (LC-OCT) combined with artificial intelligence (AI) for noninvasive diagnosis, differentiation, and longitudinal monitoring. MAT…

Artificial intelligence for healthcare: restrained development despite impressive applications.

BACKGROUND: Artificial intelligence (AI) remains poorly understood and its rapid growth raises concerns reminiscent of dystopian narratives. AI has shown the capability of producing new medical content and improving management through optimization and standardization, which shortens queues, while its complete reliance on technical solutions threatens the traditional doctor-patient bond. APPROACH: Based on the World Economic Forum's emphasis on the need for faster AI adoption in the medical field, we highlight current gaps in the understanding of its application and offer a set of priorities f…

Advancements in artificial intelligence applications for liver ultrasound imaging.

Liver diseases consistently plague people's daily lives as a result of their high morbidity and mortality rates. Ultrasound (US), favoured by its flexibility, free of radiation, cost-effectiveness, and real-time capabilities, has been commonly employed as one of the first-line imaging tools for hepatic conditions. Artificial intelligence (AI) algorithms are increasingly applied to automatically identify intricate patterns and perform quantitative analyses in US imaging, potentially reducing radiologists' workload and improving diagnostic efficiency. AI-based US has been of substantial assistan…

Искусственный интеллект и множественный склероз: прошлое, настоящее и будущее.

Искусственная разведка (ИИ) в множестве склерозов (МС) представляет собой область, которая приобретает все большее значение. Мы рассматриваем исторический контекст, текущие виды применения и будущие перспективы АИ и машинного обучения (ML) в MS. В обзоре подчеркивается потенциал АИ в плане решения важнейших проблем в области управления МЗ, включая раннюю и точную диагностику, индивидуализированные стратегии лечения, прогнозирование и эффективный контроль за пациентами. Используя большие наборы данных и высокомерные данные, МА обещает глубокое понимание и укрепляет процессы принятия клинических

Совпадение в аннотации оригинала
Artificial intelligence (AI) in multiple sclerosis (MS) is an area of growing importance of growing importance. We review the historical context, current applications, and future prospects of AI and machine learning (ML) in MS. The review highlights AI's potential to address critical challenges in MS management, including early and accurate diagnosis, indivi…

Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.

Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with 177Lu radioligands for prostate cancer and neuroendocrine tumors is routinely given as the same arbitrary fixed administered activity to every patient. Nuclear oncology, as currently practiced with 177Lu-prostate-specific membrane antigen and 177Lu peptide receptor radionuclide therapy, cannot, therefore, be characterized as personalized precision medicine. The evolution of artificial intelligence (AI) could change this "one-size-fits-all" approach to theranostics, through …

Advancing Psoriasis Care through Artificial Intelligence: A Comprehensive Review.

PURPOSE OF REVIEW: Machine learning (ML), a subset of artificial intelligence (AI), has been vital in advancing tasks such as image classification and speech recognition. Its integration into clinical medicine, particularly dermatology, offers a significant leap in healthcare delivery. RECENT FINDINGS: This review examines the impact of ML on psoriasis-a condition heavily reliant on visual assessments for diagnosis and treatment. The review highlights five areas where ML is reshaping psoriasis care: diagnosis of psoriasis through clinical and dermoscopic images, skin severity quantification, …

Perineuronal Net Microscopy: From Brain Pathology to Artificial Intelligence.

Perineuronal nets (PNN) are a special highly structured type of extracellular matrix encapsulating synapses on large populations of CNS neur … synaptic terminal. We also review some updates regarding PNN as a potential pharmacological target. Artificial intelligence (AI)-based methods are now arriving as a new tool that may have the potential to grasp the brain's complexity through a wide range of organization levels-from synaptic molecular events to large scale tissue rearrangements and the whole-brain connect …

Artificial Intelligence in Laryngology, Broncho-Esophagology, and Sleep Surgery.

Technological advancements in laryngology, broncho-esophagology, and sleep surgery have enabled the collection of increasing amounts of complex data for diagnosis and treatment of voice, swallowing, and sleep disorders. Clinicians face challenges in efficiently synthesizing these data for personalized patient care. Artificial intelligence (AI), specifically machine learning and deep learning, offers innovative solutions for processing and interpreting these data, revolutionizing diagnosis and management in these fields, and making care more efficient and effective. In this study, we review rec…

Artificial intelligence-driven electrocardiography: Innovations in hypertrophic cardiomyopathy management.

Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its heterogeneous phenotype and clinical course. Artificial Intelligence (AI) and Machine Learning (ML) techniques hold promise in transforming the role of Electrocardiography (ECG) in HCM diagnosis, prognosis, and management. AI, including Deep Learning (DL), enables computers to learn patterns from data, allowing for the development of models capable of analyzing ECG signals. DL models, such as convolutional neural networks, have shown promise in accurately identifying HCM-related abnormalities in…

Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variabi…

Oropharyngeal Cancer Staging Health Record Extraction Using Artificial Intelligence.

IMPORTANCE: Accurate, timely, and cost-effective methods for staging oropharyngeal cancers are crucial for patient prognosis and treatment decisions, but staging documentation is often inaccurate or incomplete. With the emergence of artificial intelligence in medicine, data abstraction may be associated with reduced costs but increased efficiency and accuracy of cancer staging. OBJECTIVE: To evaluate an algorithm using an artificial intelligence engine capable of extracting essential information from medical records of patients with oropharyngeal cancer and assigning tumor, nodal, and metasta…

Artificial Intelligence in Temporal Bone Imaging: A Systematic Review.

OBJECTIVE: The human temporal bone comprises more than 30 identifiable anatomical components. With the demand for precise image interpretation in this complex region, the utilization of artificial intelligence (AI) applications is steadily increasing. This systematic review aims to highlight the current role of AI in temporal bone imaging. DATA SOURCES: A Systematic Review of English Publications searching MEDLINE (PubMed), COCHRANE Library, and EMBASE. REVIEW METHODS: The search algorithm employed consisted of key items such as 'artificial intelligence,' 'machine learning,' 'deep learning,'…

Artificial intelligence in neurology: opportunities, challenges, and policy implications.

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care tra…

Generative artificial intelligence and academic writing: friend or foe?

This viewpoint examines the use of generative AI models in medical writing, discusses the opportunities and threats they represent, and highlights avenues for improvement and future research.

Artificial intelligence-driven automated lung sizing from chest radiographs.

Lung size measurements play an important role in transplantation, as optimal donor-recipient size matching is necessary to ensure the best p … s, and patient rotation. Although validation in a larger cohort is necessary, this study highlights artificial intelligence's potential to both provide reproducible lung size assessment in real patients and enable studies on the effect of lung size matching on transplant outcomes in large data sets.

[What contribution can make artificial intelligence to urinary cytology?].

Urinary cytology using the Paris system is still the method of choice for screening high-grade urothelial carcinomas. However, the use of the objective criteria described in this terminology shows a lack of inter- and intra-observer reproducibility. Moreover, if its sensitivity is excellent on instrumented urine, it remains insufficient on voided urine samples. Urinary cytology appears to be an excellent model for the application of artificial intelligence to improve performance, since the objective criteria of the Paris system are defined at cellular level, and the resulting diagnostic approa…

Artificial Intelligence-Assisted Cancer Status Detection in Radiology Reports.

UNLABELLED: Cancer research is dependent on accurate and relevant information of patient's medical journey. Data in radiology reports are of … re of reports and demand appropriate skillset. In this work, we explore state of the art methods in artificial intelligence (AI) and implement end-to-end pipeline for fast and accurate annotation of radiology reports. Language models (LM) are trained using curated data by approaching curation as multiclass or multilabel classification problem. The classification tasks ar …