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Abstract Precise extraction of medical phenotypes and entities from electronic health record (EHR) text is vital for numerous clinical research tasks, including cohort classification, tracking temporal patterns in diseas...

ABSTRACT Background The COVID‐19 pandemic has caused massive devastation worldwide, and its effects still persist. Managing the early stages was difficult, but scientists worked tirelessly to control it. The emergence ...

ABSTRACT: In recent years, the use of computing has increased along with medical skills, and this had impressive results in terms of classification and treatment, in addition to facilitating the matter of medical personn...

Abstract Objective To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography. Data source Our systematic revi...

IntroductionVisual electrophysiology, including electroretinograms (ERG) and visual evoked potentials (VEP), provides a real-time functional assessment of retinal and post-retinal pathways, complementing structural imagi...

The integration of artificial intelligence (AI) into retinopathy of prematurity (ROP) screening represents an important advancement in neonatal ophthalmology, particularly in settings facing specialist shortages and scre...

Abstract Background The ageing and growing population, rising cancer rate, and increased healthcare demand drive higher medical costs. Integrating Artificial Intelligence (AI) into oncology revolutionizes cancer diagnosi...

Introduction and aims: Oral and maxillofacial surgery (OMFS) produces complex clinical data, while artificial intelligence (AI) applications in this field remain limited despite great potential. This study aimed to map t...

Abstract Background As transcatheter aortic valve replacement (TAVR) becomes increasingly common, patients are seeking procedural information from Large Language Models (LLMs; AI systems trained on large text datasets to...

Abstract Surgical planning for clear cell renal cell carcinoma (ccRCC) is highly dependent on tumor aggressiveness. However, current decisions are predominantly based on postoperative pathology, limiting opportunities fo...

Abstract The development of Artificial Intelligence (AI) is rapidly advancing, and AI tools are being integrated into many aspects of daily life, including medical care and public health. The full extent to which AI rela...

Background Artificial intelligence (AI) is playing an increasingly important role in diagnostic imaging, helping specialists improve the quality and speed of medical services. Despite the potential of AI, electroradiolog...

Migraine is a complicated genetic disorder characterized by episodes of moderate-to-severe headaches that are usually unilateral and are frequently accompanied by nausea and increased sensitivity to sound and light. A mi...

High-quality, openly accessible clinical datasets remain a significant bottleneck in advancing both research and clinical applications within medical artificial intelligence. Case reports, often rich in multimodal clinic...

Nazgul Omarbekova,1 Berik Koichubekov,1 Khamida Abdikadirova,2 Marina Sorokina,1 Azamat Kharin,1 Manshuk Nurmaganbetova3 1Department of Informatics and Biostatistics, Karaganda Medical University, Karaganda, Kazakhstan; ...

BackgroundPredicting enterocutaneous fistula (ECF)–associated sepsis and mortality poses significant challenges in digital health care due to the disease’s complexity and heterogeneous clinical manifestations. Curren...

Abstract BackgroundGenerative artificial intelligence (GenAI) is rapidly expanding in higher education and clinical practice. However, its use during clinical placements, where cognitive demands and responsib...

Background Large language models (LLMs) have demonstrated promising capabilities in medical diagnostic reasoning, yet their performance in specialized clinical domains such as rheumatology remains incompletely characteri...

In this work, a structured hierarchy of hyperdatasets for Monkeypox image classification is presented, aiming to evaluate the robustness and generalization ability of deep classification models for Mpox detection. A syst...

Background: Recent years have witnessed a surge in the integration of digital intelligence within dental education, with various technologies increasingly supporting clinical skill training and educational applications. ...

Artificial intelligence, particularly large-language models, increasingly informs clinical decision-making, from triage to treatment recommendations. While promising efficiency and objectivity, these systems can encode a...

Abstract The development of trustworthy AI models is crucial, particularly for critical medical applications such as brain tumor detection using MRI images. However, medical images are being increasingly targeted by adve...

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with strong invasiveness, limited treatment options, and poor prognosis. Its early accurate diagnosis and individualized treatment are major challenges f...

Abstract Integration of Artificial Intelligence (AI), particularly deep learning, into medical imaging represents a profound shift in diagnostic medicine, moving from purely descriptive analysis to advanced predictive an...