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<p>Purpose of review: This review examines how artificial intelligence (AI), deep learning, robotic microscopy, and other emerging digital technologies are reshaping parasitology diagnostics. We aimed to evaluate recent advances, technological opportunities, and the potential of these tools to improve diagnostic equity in regions most affected by parasitic diseases.</p><p>Recent findings: Over the past several years, AI-driven diagnostic systems have demonstrated high accuracy in detecting malaria, leishmaniasis, schistosomiasis, and soil-transmitted helminths, often outperforming manual microscopy—particularly for low-intensity or mixed infections. Robotic and automated microscopy platforms have reduced observer variability and increased throughput, while mobile health and edge-computing approaches have expanded feasibility in low-resource settings.</p>

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Reshaping Parasitology Diagnostics with Machine Learning: A Path Toward Equity in Global Health

  • 期刊论文

  • 发表日期：

    2026年1月24日

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  • Varol Tunali

摘要

Purpose of review: This review examines how artificial intelligence (AI), deep learning, robotic microscopy, and other emerging digital technologies are reshaping parasitology diagnostics. We aimed to evaluate recent advances, technological opportunities, and the potential of these tools to improve diagnostic equity in regions most affected by parasitic diseases.

Recent findings: Over the past several years, AI-driven diagnostic systems have demonstrated high accuracy in detecting malaria, leishmaniasis, schistosomiasis, and soil-transmitted helminths, often outperforming manual microscopy—particularly for low-intensity or mixed infections. Robotic and automated microscopy platforms have reduced observer variability and increased throughput, while mobile health and edge-computing approaches have expanded feasibility in low-resource settings.