Machine learning (ML) methods are proliferating in scientific research. However, the
adoption of these methods has been accompanied by failures of validity, reproducibility, and …

Objectives Missing data is a common problem during the development, evaluation, and
implementation of prediction models. Although machine learning (ML) methods are often …

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …

Abstract Background and Objectives We sought to summarize the study design, modelling
strategies, and performance measures reported in studies on clinical prediction models …

Current and future healthcare professionals are generally not trained to cope with the
proliferation of artificial intelligence (AI) technology in healthcare. To design a curriculum …

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 …

Background Having an appropriate sample size is important when develo** a clinical
prediction model. We aimed to review how sample size is considered in studies develo** …

Background A comprehensive overview of artificial intelligence (AI) for cardiovascular
disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external …

Simple Summary The future of plant biology, particularly rapidly advancing precision
horticulture and predictive breeding, will require the transformation of huge volumes of multi …

In recent years, there has been explosive development in artificial intelligence (AI), which
has been widely applied in the health care field. As a typical AI technology, machine …