Objectives: To assess the effects of digital patient decision-support tools for atrial fibrillation (AF) treatment decisions in adults with AF.
Study Design: Systematic review and meta-analysis.
Eligibility Criteria: Eligible randomised controlled trials (RCTs) evaluated digital patient decision-support tools for AF treatment decisions in adults with AF.
Purpose: Artificial intelligence (AI) for reading breast screening mammograms could potentially replace (some) human-reading and improve screening effectiveness. This systematic review aims to identify and quantify the types of AI errors to better understand the consequences of implementing this technology.
Methods: Electronic databases were searched for external validation studies of the accuracy of AI algorithms in real-world screening mammograms.
Background: We examined whether digital breast tomosynthesis (DBT) detects differentially in high- or low-density screens.
Methods: We searched six databases (2009-2020) for studies comparing DBT and digital mammography (DM), and reporting cancer detection rate (CDR) and/or recall rate by breast density. Meta-analysis was performed to pool incremental CDR and recall rate for DBT (versus DM) for high- and low-density (dichotomised based on BI-RADS) and within-study differences in incremental estimates between high- and low-density.
Supplemental screening with MRI or ultrasound increases cancer detection rate (CDR) in women with standard screening mammography. Whether it also reduces interval cancer rate (ICR) is unclear. This study reviewed the evidence evaluating the effect of supplemental imaging on ICR in women undergoing screening mammography.
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