Background: Artificial intelligence (AI) is rapidly expanding in medicine despite a lack of
consensus on its application and evaluation. Objective: We sought to identify current …

Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in
practice is critical. Clinical evaluation aims to confirm acceptable AI performance through …

Abstract Background and Objective Medical machine learning (ML) models tend to perform
better on data from the same cohort than on new data, often due to overfitting, or co-variate …

Background The reporting of machine learning (ML) prognostic and diagnostic modeling
studies is often inadequate, making it difficult to understand and replicate such studies. To …

Objectives To date, many artificial intelligence (AI) systems have been developed in
healthcare, but adoption has been limited. This may be due to inappropriate or incomplete …

Introduction Wearable sensors have shown promise as a non-intrusive method for collecting
biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of …

Pathway enrichment analysis (PEA) is a computational biology method that identifies
biological functions that are overrepresented in a group of genes more than would be …

Most Artificial Intelligence applications are based on supervised machine learning (ML),
which ultimately grounds on manually annotated data. The annotation process is often …

Background Ovarian cancer is the fifth leading cause of mortality among women in the
United States. Ovarian cancer is also known as forgotten cancer or silent disease. The …

Objectives In recent years, periodontitis, and dental caries have become common in humans
and need to be diagnosed in the early stage to prevent severe complications and tooth loss …