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August 11, 2025F1000ResearchOpen Access

Progress and challenges for the application of machine learning for neglected tropical diseases

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Authors

CKChungYuen KhewRARahmad AkbarNMNorfarhan Mohd‐Assaad

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Overview

This review highlights machine learning's role in advancing detection and management of neglected tropical diseases, indicating significant challenges ahead.

Key Points

  • Machine learning enhances detection and management of neglected tropical diseases, indicating vast potential.
  • An estimated 1.7 billion people are affected by neglected tropical diseases annually, highlighting urgent need for intervention.
  • The review assesses current machine learning applications for disease control and the obstacles faced in implementation.
  • These findings underscore the need for improved tools to combat neglected tropical diseases and their economic implications.

Cite This Study

Khew et al. (2025) studied this question.

synapsesocial.com/papers/68c2354db210217d6476fa34https://doi.org/10.12688/f1000research.129064.3
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The Role of Big Data in Developing Innovative Predictive Learning Models for Neglected Tropical Diseases within the New Generation of the Evidence-Based Medicine Pyramid.2025
  2. 2The Burden of Neglected Tropical Diseases: A Global Review of Control Programs and Research Gaps2025
  3. 3The changing neglected tropical disease landscape in Africa: implications for policy, practice, and strengthening health systems2025
  4. 4Unlocking the blueprint to eliminating neglected tropical diseases: A review of efforts in 50 countries that have eliminated at least 1 NTD2025 · 6 citations
  5. 5Neglected Tropical Diseases in conflict-related humanitarian emergency settings: a systematic review of the literature2025 · 2 citations