AI Medical Compendium

A combi-elasto ultrasound based deep learning model on liver fibrosis staging for suspected MASLD/MASH patients.

BMC medical imaging Jul 18, 2026

OBJECTIVES: Metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) weakened the diagnostic ability of traditional elastography ultrasound on liver fibrosis. Combi-elasto ultrasound... read more

A scoping review on artificial intelligence-based tools for cardiovascular disease risk prediction.

BMC medical informatics and decision making Jul 18, 2026

BACKGROUND: Cardiovascular disease (CVD) is a leading cause of death worldwide, making early risk prediction essential for improving outcomes. Although artificial intelligence (AI) models promise to improve predictions, questions remain about interpr... read more

AMIUgraph: analysis and modeling of interactions for utility-driven benchmarking of graph-based models in healthcare.

BMC medical informatics and decision making Jul 18, 2026

BACKGROUND: Graph-based machine learning approaches, including Knowledge Graph Embedding (KGE) methods and Graph Neural Networks (GNNs), have emerged as powerful tools for modeling complex biomedical data. However, a systematic and clinically grounde... read more

Enhancing virtual screening of cystathionine β-synthase inhibitors: benchmarking target-specific machine-learning scoring functions against state-of-the-art AI docking and co-folding approaches.

Journal of cheminformatics Jul 18, 2026

Cystathionine β-synthase (CBS) has emerged as an important therapeutic target implicated in cancer and Down syndrome, yet the discovery of selective CBS inhibitors remains challenging due to limited structural diversity of known ligands and the scarc... read more

Integrating single-cell sQTL mapping with deep-learning splicing prediction identifies causal variants under influenza infection.

Genome biology Jul 18, 2026

BACKGROUND: Realizing the full potential of human genetics requires identifying causal variants and genes underlying association signals. Molecular quantitative trait locus (molQTL) analyses, such as expression QTL (eQTL) and splicing QTL (sQTL), lin... read more

Device-measured movement behaviours and cancer incidence in a population sample of UK adults: Dual 24-hour analyses of postural and intensity compositions.

BMC medicine Jul 18, 2026

BACKGROUND: Insufficient physical activity (PA) and excessive sedentary behaviour is associated with several cancers. Personalised approaches to increasing healthy movement behaviours over unhealthy behaviours may be more effective than a one-size-fi... read more

Radiomic model with SHAP-based interpretability for predicting invasiveness of pure ground-glass nodules: a retrospective study based on high-resolution computed tomography (HRCT) volumetric datasets.

Journal of cardiothoracic surgery Jul 18, 2026

BACKGROUND: Radiomics holds promise for lung cancer diagnosis. This study developed an interpretable radiomics-clinical model to predict the invasiveness of pure ground-glass nodules (pGGNs) on high-resolution computed tomography (HRCT). To address t... read more

Tumor cells escape from therapy-induced senescence: from molecular mechanisms to targeted intervention.

Biology direct Jul 18, 2026

Tumor recurrence represents a significant challenge in the field of tumor therapy, severely affecting patients' survival rates and quality of life. Even after standard treatments, many patients still experience tumor recurrence. Traditional radiother... read more

Technology anxiety and artificial intelligence readiness in nursing students: a cross-sectional study.

BMC nursing Jul 18, 2026

INTRODUCTION: As artificial intelligence (AI) becomes more common in healthcare, nursing students need to be both mentally and emotionally ready to use it. But feeling anxious about technology might hold them back. This study looked at whether there ... read more

Investigating the relationship between attitudes toward artificial intelligence and moral sensitivity among nursing students.