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Background <p>Neglected Tropical Diseases disproportionately affect populations in Africa and other low- and middle-income countries. Machine learning has potential to improve disease prediction, detection and control, but its use in neglected tropical disease research remains poorly characterized. This scoping review examines the current landscape of machine learning applications for neglected tropical diseases in Africa, identifying trends, gaps and opportunities for future research and implementation.</p> Methods <p>We conducted a scoping review using Joanna Briggs Institute methodology. PubMed and cited references were searched for studies applying machine learning to neglected tropical diseases in Africa. After screening and eligibility assessment, 77 studies were included in the qualitative synthesis.</p> Results <p>Here we show that most studies focus on schistosomiasis, leishmaniasis, lymphatic filariasis, and soil-transmitted helminthiases. Geo-risk prediction is the most common application while a few studies address disease detection and none focus on drug discovery or intervention optimization. Tree-based and Maximum Entropy models are the most frequently used and commonly reported as best performing. Most studies use small datasets. African institutional leadership, open sharing of data and source code, engagement of programmatic and policy stakeholders, and deployment of models in real-world settings remain limited.</p> Conclusions <p>Machine learning research for neglected tropical diseases in Africa remains concentrated in a few diseases and applications, with limited translation into practice. Greater investment in local capacity building, equitable collaborations, open data sharing, transfer learning, deployment-focused research, and standardized machine learning workflows could enhance the real-world impact of machine learning for neglected tropical diseases control and elimination.</p>

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Potential of machine learning for prevention and control of neglected tropical diseases: a scoping review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

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  • Ronald Galiwango,
  • Racheal Claire Kyomukama,
  • Sandra Ruth Babirye,
  • Lydia Abolo,
  • Steve Cygu Bicko,
  • Joachim Ssenkaali,
  • Onan Mulumba,
  • Moses Adriko,
  • Rapheal Opon,
  • Andrew Walakira,
  • Daudi Jjingo,
  • Agnes N. Kiragga

摘要

Background

Neglected Tropical Diseases disproportionately affect populations in Africa and other low- and middle-income countries. Machine learning has potential to improve disease prediction, detection and control, but its use in neglected tropical disease research remains poorly characterized. This scoping review examines the current landscape of machine learning applications for neglected tropical diseases in Africa, identifying trends, gaps and opportunities for future research and implementation.

Methods

We conducted a scoping review using Joanna Briggs Institute methodology. PubMed and cited references were searched for studies applying machine learning to neglected tropical diseases in Africa. After screening and eligibility assessment, 77 studies were included in the qualitative synthesis.

Results

Here we show that most studies focus on schistosomiasis, leishmaniasis, lymphatic filariasis, and soil-transmitted helminthiases. Geo-risk prediction is the most common application while a few studies address disease detection and none focus on drug discovery or intervention optimization. Tree-based and Maximum Entropy models are the most frequently used and commonly reported as best performing. Most studies use small datasets. African institutional leadership, open sharing of data and source code, engagement of programmatic and policy stakeholders, and deployment of models in real-world settings remain limited.

Conclusions

Machine learning research for neglected tropical diseases in Africa remains concentrated in a few diseases and applications, with limited translation into practice. Greater investment in local capacity building, equitable collaborations, open data sharing, transfer learning, deployment-focused research, and standardized machine learning workflows could enhance the real-world impact of machine learning for neglected tropical diseases control and elimination.