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A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis.
- Source :
- NPJ Digital Medicine; 5/14/2024, Vol. 7 Issue 1, p1-23, 23p
- Publication Year :
- 2024
-
Abstract
- Scientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and meta-analysis to evaluate the performance of AI algorithms for skin cancer classification in comparison to clinicians with different levels of expertise. Based on PRISMA guidelines, 3 electronic databases (PubMed, Embase, and Cochrane Library) were screened for relevant articles up to August 2022. The quality of the studies was assessed using QUADAS-2. A meta-analysis of sensitivity and specificity was performed for the accuracy of AI and clinicians. Fifty-three studies were included in the systematic review, and 19 met the inclusion criteria for the meta-analysis. Considering all studies and all subgroups of clinicians, we found a sensitivity (Sn) and specificity (Sp) of 87.0% and 77.1% for AI algorithms, respectively, and a Sn of 79.78% and Sp of 73.6% for all clinicians (overall); differences were statistically significant for both Sn and Sp. The difference between AI performance (Sn 92.5%, Sp 66.5%) vs. generalists (Sn 64.6%, Sp 72.8%), was greater, when compared with expert clinicians. Performance between AI algorithms (Sn 86.3%, Sp 78.4%) vs expert dermatologists (Sn 84.2%, Sp 74.4%) was clinically comparable. Limitations of AI algorithms in clinical practice should be considered, and future studies should focus on real-world settings, and towards AI-assistance. [ABSTRACT FROM AUTHOR]
- Subjects :
- MEDICAL information storage & retrieval systems
SKIN tumors
RECEIVER operating characteristic curves
DIAGNOSTIC imaging
RESEARCH funding
ARTIFICIAL intelligence
META-analysis
DESCRIPTIVE statistics
SYSTEMATIC reviews
MEDLINE
HOSPITAL medical staff
ODDS ratio
COMPUTER-aided diagnosis
MEDICAL databases
DERMOSCOPY
HISTOLOGICAL techniques
ONLINE information services
DATA analysis software
DERMATOLOGISTS
CONFIDENCE intervals
MACHINE learning
ALGORITHMS
SENSITIVITY & specificity (Statistics)
EVALUATION
Subjects
Details
- Language :
- English
- ISSN :
- 23986352
- Volume :
- 7
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- NPJ Digital Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 177250224
- Full Text :
- https://doi.org/10.1038/s41746-024-01103-x