While the opportunities of ML and AI in healthcare are promising, the growth of complex data-
driven prediction models requires careful quality and applicability assessment before they …

Abstract Background Artificial Intelligence (AI) techniques are being applied in nursing and
midwifery to improve decision‐making, patient care and service delivery. However, an …

Purpose To propose a new quality scoring tool, METhodological RadiomICs Score
(METRICS), to assess and improve research quality of radiomics studies. Methods We …

Study Design Item 5. Indicate if the study is retrospective or prospective. Evaluate predictive
models in a prospective setting, if possible. Item 6. Define the study's goal, such as model …

Background Deep learning offers considerable promise for medical diagnostics. We aimed
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …

Social media is now being used to model mental well-being, and for understanding health
outcomes. Computer scientists are now using quantitative techniques to predict the …

An emphasis on overly broad notions of generalisability as it pertains to applications of
machine learning in health care can overlook situations in which machine learning might …

Artificial intelligence and machine learning are driving a paradigm shift in medicine,
promising data-driven, personalized solutions for managing diabetes and the excess …

The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

Objectives The objective of this study was to compare performance of logistic regression
(LR) with machine learning (ML) for clinical prediction modeling in the literature. Study …