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Orthodontists' Perceptions, Attitudes, and Adoption of Artificial Intelligence: A Scoping Review.

Background: Artificial intelligence (AI) is increasingly utilized in orthodontics for diagnosis, treatment planning, and clinical management. Nevertheless, considerable variation persists in orthodontists' knowledge, attitudes, utilization patterns, recommendations, and concerns regarding AI. The aim of this scoping review was to synthesize survey-based evidence on orthodontists' perceptions and experiences with artificial intelligence. Methods: A systematic literature search was performed in PubMed, Embase, Scopus, and Web of Science to identify survey-based studies evaluating AI awareness, a…

A multimodal explainable artificial intelligence framework for interpretable Parkinson's disease prediction.

Parkinson’s disease (PD) is a complex neurological disorder that is difficult to diagnose at an early stage. Early diagnosis and prediction of the disease can help improve the prognosis of patients and therapeutic interventions. Although machine learning (ML) has shown strong potential for PD diagnosis and prediction, its clinical use is limited by the lack of model interpretability. In this study, advanced but interpretable framework is proposed for Parkinson’s disease diagnosis by incorporating explainable artificial intelligence (XAI) and machine learning methods. The proposed framework est…

Outcomes of Artificial Intelligence-Enhanced Colonoscopy in a Tertiary Clinical Setting.

INTRODUCTION: Colonoscopies reduce colorectal cancer incidence and mortality, but challenges remain in detecting all precancerous lesions. Operator fatigue, technique variability, and subtle lesions can lead to missed adenomas, highlighting the need for tools that standardize detection quality. Computer-aided detection (CADe) systems use artificial intelligence to enhance lesion detection and improve screening colonoscopy quality. This study evaluates the impact of CADe on key quality and efficiency metrics in a clinical setting. METHODS: A single-center retrospective study at an academic ter…

A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.

BACKGROUND: Cardiovascular toxicity (CVT) is a major concern after radiotherapy (RT), contributing to morbidity and mortality among cancer survivors. Artificial intelligence (AI) may improve risk prediction and RT planning; however, its role in RT-associated CVT remains unclear. This study aimed to systematically evaluate AI applications and study quality in this field. METHODS: A PRISMA-guided systematic review of PubMed, Ovid EMBASE, Cochrane Library, and Web of Science was conducted through October 1, 2025. Eligible studies included original human research in English applying AI to CVT or …

Towards accurate artificial intelligence models for strain-level phage-host prediction.

Strain-level prediction of phage-host interactions (PHIs) is essential for developing targeted phage therapies. Traditional empirical and homology-based methods often lack the resolution and scalability needed for precision applications. Recently, a new generation of artificial intelligence-driven models has emerged leveraging genomic information to infer PHIs at strain-level resolution. Here, we review recent advances in strain-level PHI prediction, spanning biologically grounded feature-based models, hybrid representation-learning frameworks, phylogeny-agnostic machine learning approaches, a…

The Growing Need to Address Mental Health Vulnerabilities to Artificial Intelligence.

Use of artificial intelligence (AI) may be contributing to mental health problems, including delusions, but reports to date have largely been anecdotal and focused on users with prior psychiatric diagnoses. Crucial questions remain with regard to the prevalence, types, predictors, outcomes, and prevention of AI-induced mental health problems, and practice, policy, educational, and research implications are not yet clear. Several of these problems may resemble those that arise with membership in cults-which similarly attract people who become deeply involved psychologically and which seek to di…

Assessing knowledge and perception of artificial intelligence in solid organ transplantation.

INTRODUCTION: Artificial intelligence (AI) is increasingly integrated into healthcare, reshaping clinical decision-making and care pathways. In solid organ transplantation (SOT), where clinical complexity and uncertainty are high, AI holds particular promise. Yet, beyond technical performance, its successful adoption depends on how it is understood and perceived by patients and healthcare professionals (HP). To date, little is known about these perspectives in SOT. This study provides the first assessment of knowledge and perceptions of AI among patients and healthcare professionals in this fi…

Artificial Intelligence for the Detection of Hypertrophic Cardiomyopathy From Standard Electrocardiogram.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is often diagnosed late, increasing avoidable risk and delaying treatment. Artificial intelligence (AI) for 12-lead electrocardiograms (ECGs) may identify undetected HCM earlier. OBJECTIVES: This study aimed to evaluate the performance of an AI algorithm for HCM detection and identify predictors of correct classification. METHODS: Of 314 patients with cardiac magnetic resonance imaging (cMRI) for suspected HCM, 150 had analyzable ECGs and confirmed HCM by physician review of medical records and cMRI (ground truth). Eighty-three control patients w…

Artificial Intelligence and FLIP Panometry-Automated Classification of Esophageal Motility Patterns.

Background/Objectives: Functional lumen imaging probe (FLIP) panometry allows real-time assessment of the esophagogastric junction opening and esophageal body contractile activity during an endoscopic procedure. Despite the development of the Dallas Consensus, FLIP panometry analysis remains complex. Artificial intelligence (AI) models have proven their benefit in high-resolution esophageal manometry; however, data on their role in FLIP panometry are scarce. This study aims to develop an AI model for automatic classification of motility patterns during a FLIP panometry exam. Methods: A total o…

Endo-Hepatology and Artificial Intelligence: New Avenues for Diagnosis and Intervention.

Artificial intelligence (AI) is gaining momentum in the field of endo-hepatology, offering potential improvements in diagnosis, risk stratification, and procedural outcome prediction. This review outlines AI applications across endoscopic domains including variceal screening, endoscopic retrograde cholangiopancreatography (ERCP), cholangioscopy, and metabolic interventions, with particular promise seen in image-based models and radiomics. While early studies-mostly retrospective and pilot in nature-demonstrate improved accuracy and efficiency over conventional methods, only a few have progress…

Diagnostic performance of real-time characterization in artificial intelligence-assisted colonoscopy.

BACKGROUND AND STUDY AIMS: Because most colorectal polyps are diminutive and carry minimal cancer risk, artificial intelligence (AI) might enable diagnostic strategies such as leave-in-situ and resect-and-discard, provided it meets predefined quality benchmarks. We assessed diagnostic performance of a computer-aided polyp characterization system (CADx) in predicting histology of diminutive rectosigmoid polyps during colonoscopy in a Danish population. PATIENTS AND METHODS: We conducted a prospective diagnostic accuracy study across four endoscopy centers. Adults referred for colonoscopy due t…

Applications of artificial intelligence in robot-assisted surgery: a systematic review.

To characterize applications of artificial intelligence (AI) in robot-assisted surgery, summarize technical and clinical performance, and assess the quality of the available evidence. PubMed, Web of Science Core Collection, and Scopus were searched for English-language journal articles published from 1 January 2020 through 31 October 2025. Randomized, observational, model-development, validation, and feasibility studies evaluating AI in robot-assisted surgery or closely related image-guided minimally invasive workflows were eligible. Two reviewers independently performed study selection, data …

Artificial intelligence for optimization of immunotherapy: current applications and transformative potential.

Artificial intelligence (AI) is a transformative technology that has captivated the medical world with its potential to optimize cancer treatment and enhance precision oncology. In cancer diagnosis and treatment, various AI technologies have already provided high-level data examination and analytics that preceding innovations were not capable of. Cancer immunotherapy is a treatment that seeks to boost the immune system to recognize and eradicate tumors. It is a field that is constantly evolving, serving as a fertile environment where AI technologies can accelerate discovery and personalize its…

Cancer Vaccine Development: Toward Artificial Intelligence-Assisted Personalized Cell Membrane Nanovaccine.

Cancer vaccines are an attractive immunotherapeutic approach, and numerous clinical trials are ongoing. However, most candidates have not ac … mor antigen prediction alone, a longstanding bottleneck in cancer vaccine development. In parallel, artificial intelligence (AI) is driving rapid advances across biomedical engineering and is applied to cancer nanovaccine research, including antigen identification, nanoparticle design optimization, and immune response modeling. This review comprehensively summarizes rece …

USING ARTIFICIAL INTELLIGENCE TO ASSESS MACULAR EDEMA TREATMENTS IN RETINITIS PIGMENTOSA.

PURPOSE: This study validates a deep learning-based artificial intelligence tool for quantifying macular edema (ME) intraretinal fluid (IRF) volumes in retinitis pigmentosa, and through longitudinal analysis of IRF, provides new insight into treatment efficacy and disease natural history. METHODS: This retrospective, longitudinal study identified patients with retinitis pigmentosa with ME. A commercially available retinal analysis tool quantified IRF and was validated for segmentation of ME using spectral-domain optical coherence tomography volume scans. Baseline analysis of IRF versus tradit…

Artificial Intelligence Across the Echocardiographic Workflow: A Narrative Review for Clinicians.

Echocardiography is one of the most commonly used diagnostic methods in cardiovascular diseases because it is non-invasive, widely available, and capable of providing real-time assessment of function and cardiac structure. Despite these advantages, conventional echocardiography is often limited by operator dependence, interobserver inconstancy, the time-intensive nature of manual acquisition and measurement. Recent advances in artificial intelligence (AI), particularly deep learning, have created new opportunities to automate multiple steps of the echocardiographic workflow, starting from imag…

Cost-effectiveness of Artificial Intelligence applications in healthcare: A systematic review.

ObjectiveWith increasing emphasis on value-based healthcare and rising costs, it is essential to assess the economic impact of artificial intelligence (AI). This study systematically reviews the evidence on the cost-effectiveness of AI applications in healthcare.MethodsA systematic search of PubMed, Scopus and Web of Science was conducted up to 21 August 2025. Studies that had full text and were peer-reviewed articles in English and reported formal economic evaluations, including cost-effectiveness, cost-utility or cost-benefit analysis of AI-based healthcare interventions, were included in th…

Applications of generative artificial intelligence in chronic pain: a scoping review.

Chronic pain is a complex biopsychosocial condition that imposes substantial clinical and socioeconomic burdens worldwide. Although digital health interventions increasingly support chronic pain management, the role of generative artificial intelligence (AI) remains insufficiently characterized. This scoping review mapped applications of generative AI in chronic pain and identified research gaps. PubMed, Scopus, and Web of Science were searched on October 19, 2025, using "chronic pain" combined with five named generative AI systems. Primary research studies published in English that focused on…

Унификация одиссеи: искусственный интеллект для диагностирования и лечения редких заболеваний.

Подытожить текущие проблемы в диагностике и лечении редких заболеваний (РД), осветить последние достижения в области искусственного интеллекта (АИ) для РД и предложить модель будущего состояния лечения пациентов РД. Многодисциплинарный экспертный обзор с кратким изложением современных практических задач и мер по ограничению ставок в области РД-клиентов с учетом ключевых клинических и исследовательских соображений в отношении нормативных и экономических ограничений. 10,000 известные РД оказывают коллективное воздействие 1 В 10 Американцы, в целом 30 million Люди. Ежегодно на РД приходится свыше

Совпадение в аннотации оригинала
PurposeTo summarize current challenges in rare disease (RD) diagnosis and therapy, highlight recent advances in artificial intelligence (AI) for RDs, and propose a model future state for RD patient care.MethodsMultidisciplinary expert-led narrative review summarizing modern practical challenges and rate-limiting steps in RD patient care, citing key clinical …