Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 48,341 to 48,350 of 224,199 articles

Interpretable Ensemble Machine Learning Prediction of Nonadherence and the Risk of Nonpersistence of Targeted Disease-Modifying Antirheumatic Agents in Older Adults With Rheumatoid Arthritis.

Clinical therapeutics
PURPOSE: Ensemble machine learning (ML) demonstrated potential for improving predictions based on big health care data. We developed and validated interpretable ensemble ML models in evaluating the nonadherence and nonpersistence of biological or tar... read more 

Metabolic factor-based machine learning model for mortality prediction in acute hepatitis E: Development and validation from a dual-center cohort.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: Hepatitis E virus (HEV) infection remains a major cause of liver failure with high short-term mortality, yet predictive models incorporating systemic metabolic factors are limited. AIMS: We aimed to develop a machine learning model incorp... read more 

Distinction Between Benign and Borderline/Malignant Phyllodes Tumor in Breast Mammography and Ultrasound Based on Radiomics Methods.

Academic radiology
RATIONALE AND OBJECTIVES: This study aims to evaluate whether radiomics methods used on breast mammography (MG) and ultrasound (US) could distinguish between benign and borderline/malignant phyllodes tumors (PTs). MATERIALS AND METHODS: A total of 36... read more 

Multimodal CT for Predicting Microvascular Invasion in Solitary cHCC-CCA: Dual-Center External Validation.

Academic radiology
RATIONALE AND OBJECTIVES: Preoperative prediction of microvascular invasion (MVI) in combined hepatocellular-cholangiocarcinoma (cHCC-CCA) remains difficult, and externally validated CT-based tools are scarce. To develop and externally validate a mul... read more 

Multiparametric MRI-Based Deep Learning and Radiomics for Predicting Progression-Free Survival Benefit in Patients with Hepatocellular Carcinoma Treated with Immunotherapy and Targeted Therapy Plus Transarterial Chemoembolization: A Bicentric Study.

Academic radiology
RATIONALE AND OBJECTIVES: TheĀ non-invasiveĀ biomarkers for predicting progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC) treated with immunotherapy and molecular targeted therapy combined with transarterial chemoembolizati... read more 

Identifying a better-prognosis pancreatic cancer from its benign inflammatory mimic: a machine learning approach with contrast-enhanced ultrasound for early intervention.

Abdominal radiology (New York)
PURPOSE: Hypervascular pancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP) represent a classic diagnostic mimicry on contrast-enhanced ultrasound, as both exhibit similar arterial-phase hyperenhancement, precluding reliable vi... read more 

The AI Act and the MDR post-market requirements for semiautonomous AI SaMD: a radiology case study in prostate cancer.

Abdominal radiology (New York)
PURPOSE: To clarify overlapping post-market obligations under the EU Artificial Intelligence Act (AIA) and EU Medical Device Regulation (MDR) for high-risk artificial intelligence (AI) Software as a Medical Device (SaMD), and to map the regulatory la... read more 

Preoperative prediction of preserved renal parenchymal volume via multilevel CT feature fusion: a proof-of-concept study.

Abdominal radiology (New York)
PURPOSE: Accurate preoperative prediction of preserved renal parenchymal volume (RPV) following partial nephrectomy (PN) is critical for individualized postoperative management. However, current assessment approaches remain limited in precision and g... read more 

Vision transformer-based diagnosis of psoriasis and eczema in whole-slide histology.

Virchows Archiv : an international journal of pathology
Psoriasis and eczema are chronic inflammatory skin diseases with overlapping histopathological features, which often lead to diagnostic uncertainty even among experienced dermatopathologists. To address this challenge, we developed a computer-assiste... read more