Latest AI and machine learning research in pulmonology for healthcare professionals.
PURPOSE OF REVIEW: A central challenge in systemic sclerosis (SSc) is the inability to distinguish active, potentially reversible disease, from damage, irreversible fibrosis. Current imaging modalities, including high-resolution computed tomography (HRCT) and echocardiography, predominantly capture structural damage and cannot resolve this distinction. This review outlines next generation imaging ...
BACKGROUND: Large language models (LLMs) are increasingly applied in clinical decision support, yet their diagnostic performance in Chinese-language settings and under realistic clinical workflows remains unclear. In particular, how LLMs perform across diseases with different prevalence and under stepwise diagnostic processes has not been well characterized. OBJECTIVE: This study aimed to evaluate...
BACKGROUND: Real-time breath metabolomic profiling may detect lung cancer-associated breath features, but exploratory case-control findings require ca...
BACKGROUND: Cardiovascular magnetic resonance (CMR) is the reference standard for assessing cardiac function, yet its widespread clinical use remains ...
BACKGROUND AND AIM: Atrial fibrillation (AF) affects over 37 million people internationally and confers increased risk of cardiovascular conditions. P...
OBJECTIVE: This study aimed to identify trajectories of multimorbidity following acute myocardial infarction (AMI), using explainable temporal machine...
BACKGROUNDS AND OBJECTIVES: Childhood obesity and respiratory tract infections (RTIs) are 2 major global public health issues that frequently co-occur...
Early screening can significantly reduce the severe morbidity and mortality of respiratory diseases and alleviate the burden on public healthcare syst...
Activated sludge systems treating municipal wastewater frequently experience performance dysfunction, yet conventional diagnostics rely on isolated pa...
Synthetic CT (sCT) generation from cone-beam CT (CBCT) has emerged as a promising enabler for adaptive radiotherapy. While deep learning (DL) methods ...
BACKGROUND AND OBJECTIVE: Automated respiratory sound classification based on deep learning is critical for enhancing diagnostic precision and efficie...
BACKGROUND: Chronic diseases account for approximately 90% of the $4.5 trillion annual healthcare expenditure in the United States. While traditional ...
Idiopathic inflammatory myopathies (IIM) are heterogeneous immune-mediated diseases characterized by muscle inflammation that coexists with variable t...
PURPOSE: Patient-ventilator asynchrony (PVA), a mismatch between a patient's respiratory effort and the ventilator's support, affects up to 85% of pat...
Extracting disease labels from radiology reports is essential for developing deep learning-based diagnostic models and enabling large-scale retrospect...
BACKGROUND: Low- and middle-income countries face a growing dual burden of communicable and non-communicable diseases, while services remain verticall...
Electrocatalysis plays a pivotal role in sustainable energy conversion technologies; however, the rational design of high-performance electrocatalysts...
BACKGROUND: Spirometry is the standard physiological test defining airflow obstruction, the key criterion for diagnosing chronic obstructive pulmonary...
As artificial intelligence systems are increasingly used to guide decisions, it is essential that they follow ethical principles. A core principle in ...
BACKGROUND: Chronic obstructive pulmonary disease (COPD) management is complex and rapidly evolving. ChatGPT is a large language model (LLM) shown to ...