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Document Type

Review

Abstract

Introduction and Objectives: Malaria remains a life-threatening medical emergency, particularly in Plasmodium falciparum infections, with 282 million cases and approximately 610,000 deaths recorded globally in 2024. Accurate laboratory diagnosis is critical, yet conventional methods including microscopy and rapid diagnostic tests are constrained by operator dependency, low sensitivity, and emerging HRP2/HRP3 gene deletions. This scoping review maps current and emerging malaria diagnostic technologies, evaluates their performance, and identifies implementation challenges and knowledge gaps.

Methodology: Following the Arksey and O'Malley framework and PRISMA-ScR guidelines, PubMed, Scopus, and Web of Science were searched for publications from 2000 to 2026. Eligible studies involved human malaria diagnosis, reported at least one diagnostic accuracy measure, and compared two or more methods. Findings were narratively synthesized to map diagnostic technologies, reported performance characteristics, implementation challenges, and future directions.

Results: Twenty-seven studies were included. Microscopy performance was highly operator dependent with reduced sensitivity especially in low transmission settings. RDTs were rapid and affordable but showed variable diagnostic accuracies, particularly for low density infections. PCR was consistently reported among the most sensitive diagnostic methods across the included studies and frequently served as the reference standard in diagnostic evaluations. LAMP offered a field-feasible molecular alternative with simpler instrumentation. Emerging technologies including CRISPR-based assays, AI-assisted microscopy, and biosensor platforms demonstrated encouraging diagnostic performance with point-of-care potential.

Conclusions: A clear transition toward sensitive, portable, and digitally integrated malaria diagnostics is evident. While PCR sets the sensitivity benchmark, operational constraints limit its field deployment. Scalable innovations such as LAMP, NGS, CRISPR assays, and AI-assisted microscopy are better positioned to support decentralized malaria diagnosis and elimination efforts. Large-scale field validation and integration into national programs remain critical priorities for emerging technologies.

Receive Date

18 May 2026

Revise Date

30 June 2026

Accept Date

17 July 2026

Publication Date

8-25-2026

References

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