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Comparison of AI-based Chatbot Performance in Analyzing Clinical Scenarios versus Medical Residents: A Novel Approach in Chest Diseases Education.

OBJECTIVE: Rapid advancements in artificial intelligence (AI) technologies offer new opportunities in medical education. The aim of this study is to compare the performance of large language models, specifically ChatGPT-4 and Gemini, in analyzing clinical scenarios with that of chest diseases research assistants (residents), and to evaluate their potential roles in medical education. MATERIAL AND ...

[Expert consensus on optimizing the multidisciplinary clinical pathways and management models for pulmonary function testing].

The promotion and application of pulmonary function tests (PFTs) in China have achieved preliminary success;however, numerous deficiencies persist in multidisciplinary PFT clinical application pathways and management. Current challenges remain in identifing target population, implementing pathways within and outside medical institutions, allocating hardware, training personnel, managing informatio...

Artificial Intelligence in Asthma: From Diagnosis to Management.

Artificial intelligence (AI) offers new opportunities to improve asthma management across the care process. This review synthesizes evidence on AI app...

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions.

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chr...

Exploring AI and ML in managing overlap between cardiovascular disease and asthma or COPD: a scoping review.

Cardiovascular disease (CVD) is a major comorbidity in asthma and chronic obstructive pulmonary disease (COPD), yet the contribution of artificial int...

From phenotypes to trajectories: Machine learning in asthma beyond labels.

Gene expression and machine learning techniques uncover corneal biomarkers associated with oxidative stress in the myopia progression.

This study aimed to identify and validate corneal biomarkers underlying myopia using human corneal tissues. Gene expression data were analyzed to inve...

Benchmarking Hybrid CNN-Transformer Versus Pure Transformer Architectures for Accelerated Hyperpolarized 129Xe MRI Reconstruction.

BACKGROUND: Hyperpolarized 129Xe MRI faces technical challenges including low signal-to-noise ratio and breath-hold constraints. Current literature fo...

Explainable machine learning for long-term cardiovascular disease risk prediction in Chinese middle-aged and older adults: a 9-year longitudinal cohort study with web-based risk calculator.

Cardiovascular disease represents the leading cause of mortality in China, accounting for over 40% of all deaths. Existing risk prediction models pred...

Severe COVID-19 in the Republic of Korea: Epidemiology, Risk Factors, Therapeutics, and Prognostic Models From Nationwide Data.

Severe coronavirus disease 2019 (COVID-19) has posed ongoing clinical and public health challenges worldwide, with Korea providing a unique perspectiv...

FTO-CHRM3 axis regulates multiple myeloma progression: a machine learning-based identification.

BACKGROUND: Fat Mass and Obesity-Associated (FTO) is linked to multiple myeloma (MM) progression, but its action mechanisms are poorly understood. MET...

Communication, compassion, and shared decision-making in allergy.

Shared decision-making (SDM) is a structured approach to clinical care that integrates the best available evidence, clinician expertise, and patient v...

Teriparatide reduces spinal neuroinflammatory phenotypes in ovariectomized rats revealed by an artificial intelligence-driven glial cell morphometry of bright-field tissue sections.

Osteoporosis frequently presents with lower back pain, often accompanied by hypersensitivity, even in the absence of vertebral fractures, suggesting t...

Integrating bioinformatics, machine learning and molecular biology to elucidate the anti-allergic mechanisms of ginkgo biloba leaves.

Allergic diseases, encompassing allergic asthma, atopic dermatitis, food allergies, have emerged as substantial global public health challenges, with ...

Explainable Artificial Intelligence for Predicting Length of Stay and Treatment Response in Pediatric Asthma and Allergic Rhinitis: An Irregular Fuzzy Cellular Automata Approach Across Pre- and During-COVID-19 Periods.

OBJECTIVE: The COVID-19 pandemic significantly altered treatment strategies and healthcare utilization patterns in pediatric respiratory diseases. Giv...

Development and Interpretation of a Machine Learning-Based Predictive Model Using Clinical Parameters for Eosinophilic Chronic Rhinosinusitis With Nasal Polyps.

ObjectiveWe aim to predict eosinophilic chronic rhinosinusitis with nasal polyps (ECRSwNP) employing preoperative clinical parameters and machine lear...

[Care for uncontrolled asthma in Germany - with focus on Magdeburg and Mannheim regions].

Description of treatment and prescription patterns among asthma patients in the regions of Magdeburg (MD) and Mannheim (MA) compared nationwide.We ana...

Molecular and immunological heterogeneity of eosinophilic esophagitis: Insights and subtyping.

BACKGROUND: Eosinophilic esophagitis (EoE) and its related subtypes-such as EoE-like esophagitis, lymphocytic esophagitis, and nonspecific esophagitis...

AI-Enhanced Conversational Agents for Personalized Asthma Support in People With Asthma: Factors for Engagement, Value, and Efficacy in a Cross-Sectional Survey Study.

BACKGROUND: Asthma-related deaths in the United Kingdom are the highest in Europe, and only 30% of patients access basic care. There is a need for alt...

CT image-based machine learning models for predicting blood eosinophil levels in acute exacerbation of chronic obstructive pulmonary disease.