Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
OBJECTIVE: Interpretability and reproducibility remain major challenges in applying deep neural network (DNN) to neuroimaging-based diagnosis. This study proposes a radiomics-guided dual-channel deep neural network (RDDNN) to improve feature transparency and enhance clinical understanding in the classification of Parkinsonian syndromes. METHODS: In this bi-centric study, we analysed two independen...
Artificial intelligence (AI) has rapidly advanced in healthcare, demonstrating significant potential in analyzing large, heterogeneous datasets using optimized algorithms for disease prediction and personalized treatment. Assisted reproductive technology (ART), particularly in vitro fertilization (IVF) and embryo transfer, generates extensive data, making it especially suitable for AI-driven analy...
BACKGROUND AND OBJECTIVES: Social determinants of health (SDOH) are key drivers of health inequities, shaping disparities in patient outcomes that mus...
This article explores the application of big data and analytics in ambulatory medicine, population health, and inpatient medicine. The article highlig...
OBJECTIVE: Develop a causal machine learning (causal ML) framework for estimating how a diagnosis (cancer in this study) affects the likelihood of rec...
BACKGROUND: Length of stay (LOS) is a substantial driver of costs following primary total knee arthroplasty (TKA), leading to increased efforts target...
Hospital waste management (HWM) is critical to advancing environmental sustainability, particularly as Germany and the European Union (EU) pursue carb...
AIMS: To develop and validate a machine learning-based risk prediction model for delirium in older inpatients. DESIGN: A prospective cohort study. MET...
BACKGROUND: Despite KDIGO (Kidney Disease: Improving Global Outcomes) recommendations for renin-angiotensin-aldosterone system inhibitors (RAASi's) an...
INTRODUCTION: Standard spine surgery machine learning (ML) models often rely on structured clinical data, overlooking nuanced free text, such as preop...
BackgroundThe future of artificial intelligence in medicine includes the use of machine learning and large language models to improve diagnostic accur...
BACKGROUND AND OBJECTIVES: Bone metastases, affecting more than 4.8% of patients with cancer annually, and particularly spinal metastases require urge...
BACKGROUND: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are severe mucocutaneous reactions primarily triggered by drugs or inf...
BACKGROUND: Accurate and rapid phenotyping of venous thromboembolism (VTE) in longitudinal studies is important. A natural language processing (NLP) t...
Coronary CT angiography is widely implemented, with an estimated 2.2 million procedures in patients with stable chest pain every year in Europe alone....
BACKGROUND: Hypocalcemia occurs frequently in intensive care units (ICUs) and is independently associated with excess mortality. Conventional severity...
BACKGROUND AND OBJECTIVES: The goal of this study was to develop a highly precise, dynamic machine learning model centered on daily transcranial Doppl...
Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes and a leading cause of vision impairment worldwide. Despite advancem...
PURPOSE OF REVIEW: Interstitial lung disease (ILD) presents significant diagnostic and therapeutic challenges due to underlying biological heterogenei...
This review summarizes AI-supported non-pharmacological interventions for adults with chronic rheumatic diseases, detailing their components, purpose,...