What does this research mean for the field?

Artificial intelligence and machine learning models demonstrate substantial diagnostic accuracy and promising prognostic capability for sickle cell disease, though current applications remain at a proof-of-concept stage requiring prospective, external validation. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This review aims to assess the diagnostic and predictive performance of AI and ML technologies for detecting sickle cell disease.

June 1, 2026CureusOpen Access

Evaluating the Diagnostic Performance of AI and Machine Learning in Sickle Cell Disease Detection: A Systematic Review

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Authors

NANajat Elamin Abdelgader Adam OOOmer Kamal Ahmed OmerLuton and Dunstable University Hospital NHS Foundation Trust FAFayyas Ahamed

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Implication

Systematic review evaluates AI and ML diagnostic accuracy in sickle cell disease, highlighting potential for clinical use.

Key Points

This review aims to assess the diagnostic and predictive performance of AI and ML technologies for detecting sickle cell disease.
Systematic search conducted in PubMed, Web of Science, Scopus, and Embase for studies from 2021-2025.
Included original research employing AI/ML models for SCD detection, classification, and prognostic predictions.
Risk of bias assessed using the PROBAST tool; narrative synthesis performed due to methodological heterogeneity.
Seventeen studies analyzed, with accuracies exceeding 94% for image-based detection and AUC-ROC values reaching up to 0.99.
Prognostic models for mortality showed C-indices of 0.76 and AUCs of 0.77.
Fourteen studies had low overall risk of bias, while three had high risk due to small sample sizes.

Cite This Study

Adam et al. (2026) studied this question.

synapsesocial.com/papers/6a1d218f02fbce9130637969 https://doi.org/10.7759/cureus.109880

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Evaluating the Diagnostic Performance of AI and Machine Learning in Sickle Cell Disease Detection: A Systematic Review

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Key Points

Cite This Study

Evaluating the Diagnostic Performance of AI and Machine Learning in Sickle Cell Disease Detection: A Systematic Review

Discussion

Member takes

Implication

Key Points

Cite This Study

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