Publications by authors named "Selim Soyturk"
Article Synopsis
- This systematic review explores the use of artificial intelligence (AI) in diagnosing and treating urolithiasis (kidney stones), highlighting its potential to improve accuracy and effectiveness in medical practice.
- The study analyzed 71 original AI research papers, revealing that AI achieved high precision rates in stone composition (88.2%) and detection (96.9%), as well as solid accuracy in predicting stone passage and treatment outcomes (around 82-87%).
- The findings suggest that AI could significantly enhance management strategies for urolithiasis, but further research is needed to cement its role in routine clinical use.
Background: To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study.
Methods: This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library.