Washington , D.C. - State and school district leaders need to press for guardrails on AI use in schools, while also acknowledging that the technology’s rapid development makes teacher training critical, witnesses at a U.S. Senate hearing said Tuesday. The hearing—organized by the Senate Subcommittee on Education & the American Family—examined the adjustments policymakers need
A scoping review of traditional and artificial intelligence methods in malaria diagnostics – npj Digital Medicine
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- Fangxu Xing1 na1,
- Shahar Lazarev2 na1 &
- Jonghye Woo1
npj Digital Medicine (2026) Cite this article
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Abstract
Malaria remains a substantial global health burden with current diagnostics having notable limitations. Microscopy is labor-intensive and operator-dependent; rapid diagnostic tests lack sensitivity and provide qualitative rather than quantitative results. Recent AI advances, particularly deep learning, demonstrate significant potential for malaria diagnostics through automatic parasite detection in blood smears. Numerous systems achieve outstanding accuracy—comparable to human experts—while increasing throughput and reducing costs. In endemic regions, AI-based diagnostics can expand testing access; in non-endemic settings, they assist clinicians rarely encountering malaria, potentially reducing misdiagnosis rates. Successful AI applications emphasize digital medicine’s broader potential to address global health disparities through automated, expert-quality diagnostics. Yet, challenges remain—inconsistent dataset annotation standards and limited representation of diverse endemic regions—for widespread AI-based diagnostic adoption. This review examines current diagnostic methods and evaluates the translational potential of AI-driven innovations in malaria diagnostics, discussing practical implications for researchers and stakeholders seeking to integrate these advances into clinical practice.
Acknowledgements
This work was supported by a research contract with Noul Inc. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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J.W. serves as a member of the Scientific Advisory Board for Noul Inc. The remaining authors declare no competing interests. J.W. is an Editorial Board Member of npj Digital Medicine and was not involved in the journal’s review of, or decisions related to, this manuscript.
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Xing, F., Lazarev, S. & Woo, J. A scoping review of traditional and artificial intelligence methods in malaria diagnostics. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02880-3
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DOI: https://doi.org/10.1038/s41746-026-02880-3
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