AI-assisted decision support for sickle cell disease severity stratification using routine blood tests: a systematic review and meta-analysis

Stratifying sickle cell disease (SCD) severity remains challenging, particularly in resource-limited settings. Artificial intelligence (AI) models using routine complete blood count (CBC) parameters have been proposed as accessible tools for risk stratification; however, their overall performance and clinical applicability remain uncertain. We conducted a PRISMA-DTA 2020–compliant systematic review and meta-analysis (PROSPERO: CRD420251078389) including 60 studies (25,354 patients across 15 countries, 2010–2025). AI models based on CBC parameters were evaluated against heterogeneous reference standards, including clinical severity classifications and event-based outcomes. Risk of bias was assessed using QUADAS-2 and PROBAST-AI. Pooled estimates were generated using hierarchical summary receiver operating characteristic (HSROC) models. The pooled area under the curve (AUC) was 0.87 (95% CI: 0.84–0.90), with sensitivity 0.83 (0.79–0.86) and specificity 0.85 (0.81–0.88). Performance varied by setting, with higher accuracy in high-income countries (AUC 0.90) compared with low- and middle-income settings (AUC 0.82; p < 0.001). Red cell distribution width and platelet count were consistently identified as important predictors. However, substantial heterogeneity in outcome definitions and reference standards limits interpretability. AI models using routine CBC parameters demonstrate promising analytical performance for SCD risk stratification. However, the absence of a universally accepted severity gold standard, variability in outcome definitions, and differences across healthcare settings limit direct clinical applicability. These findings support cautious prospective validation and clinical utility assessment before consideration of broader real-world implementation. Not applicable. Why was this study done? - Sickle cell disease affects over 20 million people worldwide, with 75% in low-resource countries. - Current severity assessment methods are either subjective or expensive. - Artificial intelligence (AI) using routine blood tests could provide a low-cost solution. What did the researchers do and find? - We analyzed 60 studies involving 25,354 patients from 15 countries. - AI models using routine blood counts could predict disease severity with 87% accuracy. - Two key blood markers were most important: red cell width (RDW) and platelet count. - The approach may substantially reduce costs compared with specialized laboratory and genetic testing approaches, although formal cost-effectiveness studies are still required. - Performance was better in high-income countries (90% accuracy) than in low-resource settings (82%). What do these findings mean? - AI tools using simple blood tests could make severity assessment accessible everywhere. - Local adaptation is needed for use in different regions. - These tools show analytical promise but require prospective clinical validation before implementation. - Local adaptation and rigorous utility studies are essential before deployment in any setting - These findings may help inform the future development and prospective evaluation of AI-assisted clinical decision support tools using routine hematological parameters in resource-limited settings.