Abstract
Sickle cell disease (SCD) is a major global health burden, and early, accurate diagnosis is critical for effective management. Conventional diagnostic methods are often resource-intensive and inaccessible in high-burden, low-resource settings. Artificial intelligence (AI) and machine learning (ML) technologies have emerged as promising tools to automate and enhance SCD detection. This systematic review aimed to critically evaluate the diagnostic and predictive performance of AI and ML models for SCD detection and to assess their methodological quality and readiness for clinical implementation.
A systematic search of PubMed, Web of Science, Scopus, and Embase was conducted for studies published between 2021 and 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Original research employing AI/ML models for SCD detection, classification, severity stratification, or outcome prediction was included. Data on study characteristics, model types, and diagnostic performance metrics were extracted. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). A narrative synthesis was performed due to substantial methodological heterogeneity precluding meta-analysis.