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Machine Learning-Enabled Fuhrman Grade in Clear-cell Renal Carcinoma Prediction Using Two-dimensional Ultrasound Images.

Ultrasound in medicine & biology
OBJECTIVE: Accurate assessment of Fuhrman grade is crucial for optimal clinical management and personalized treatment strategies in patients with clear cell renal cell carcinoma (CCRCC). In this study, we developed a predictive model using ultrasound...

The knowledge and perception of patients in Malta towards artificial intelligence in medical imaging.

Journal of medical imaging and radiation sciences
INTRODUCTION: Artificial intelligence (AI) is becoming increasingly implemented in radiology, especially in image reporting. Patients' perceptions about AI integration in medical imaging is a relatively unexplored area that has received limited inves...

Can AI-Generated Clinical Vignettes in Japanese Be Used Medically and Linguistically?

Journal of general internal medicine
BACKGROUND: Creating clinical vignettes requires considerable effort. Recent developments in generative artificial intelligence (AI) for natural language processing have been remarkable and may allow for the easy and immediate creation of diverse cli...

Machine-Learning Model Identifies Patients With Alpha-1 Antitrypsin Deficiency Using Claims Records.

COPD
Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify patients with rare diseases using large-scale, real-world databases, whereas electronic medical reco...

Influence of panic disorder and paroxetine on brain functional hubs in drug-free patients.

Journal of psychopharmacology (Oxford, England)
BACKGROUND: The effects of panic disorder (PD) and pharmacotherapy on brain functional hubs in drug-free patients, and the utility of their degree centrality (DC) in diagnosing and predicting treatment response (TR) for PD, remained unclear.

Identification of four novel acute-on-chronic liver failure clusters with distinct clinical trajectories and mortality using machine learning methods.

Alimentary pharmacology & therapeutics
BACKGROUND AND AIMS: Machine learning (ML) can identify the hidden patterns without hypothesis in heterogeneous diseases like acute-on-chronic live failure (ACLF). We employed ML to describe and predict yet unknown clusters in ACLF.

Interpretable machine learning model predicting immune checkpoint inhibitor-induced hypothyroidism: A retrospective cohort study.

Cancer science
Hypothyroidism is a known adverse event associated with the use of immune checkpoint inhibitors (ICIs) in cancer treatment. This study aimed to develop an interpretable machine learning (ML) model for individualized prediction of hypothyroidism in pa...

From Biosensors to Robotics: Pioneering Advances in Breast Cancer Management.

Sensors (Basel, Switzerland)
Breast cancer stands as the most prevalent form of cancer amongst females, constituting more than one-third of all cancer cases affecting women. It causes aberrant cell development, which can assault or spread to other sections of the body, perhaps l...

Trial Factors Associated With Completion of Clinical Trials Evaluating AI: Retrospective Case-Control Study.

Journal of medical Internet research
BACKGROUND: Evaluation of artificial intelligence (AI) tools in clinical trials remains the gold standard for translation into clinical settings. However, design factors associated with successful trial completion and the common reasons for trial fai...