Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis

arXiv:2405.05658v1 Announce Type: cross
Abstract: Purpose: Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks.
Methods: Medline, Embase, Cochrane library and Web of Science were searched until September 2021 for studies that temporally or externally …

abstract accuracy aim analysis artificial artificial intelligence arxiv cs.cv detection diagnostic eess.iv intelligence meta meta-analysis neuroimaging patient review studies tasks test type world