Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
University of Leeds · Leeds Teaching Hospitals NHS Trust · +2 more institutions
Abstract
Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the…
Citation impact
- FWCI
- 61.74
- Percentile
- 100%
- References
- 151
Authors
8- CMClare McGenityCorresponding
University of Leeds, Leeds Teaching Hospitals NHS Trust, Leeds Beckett University
- ELEmily L. Clarke
University of Leeds, Leeds Teaching Hospitals NHS Trust
- CJCharlotte Jennings
University of Leeds, Leeds Teaching Hospitals NHS Trust
- GAGillian A. Matthews
Leeds Teaching Hospitals NHS Trust
- CCCaroline Cartlidge
University of Leeds
Topics & keywords
- Meta-analysis
- Data extraction
- Diagnostic accuracy
- Digital pathology
- Bivariate analysis
- Raw data
- Medicine
- Artificial intelligence