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Background <p>Skin-manifesting neglected tropical diseases (NTDs) pose significant diagnostic challenges due to overlapping clinical presentations and limited access to specialist care in endemic regions. Artificial intelligence (AI) has shown promise in dermatological diagnosis; however, concerns remain regarding algorithmic bias, reduced accuracy in darker skin tones, and lack of transparency in decision-making.</p> Aim <p>This review aimed to synthesise existing evidence on explainable AI approaches for the differential diagnosis of skin-manifesting NTDs, with emphasis on performance, equity across dark skin tones, and clinical applicability.</p> Methods <p>A structured narrative review was conducted using systematic search methods across PubMed/MEDLINE, Scopus, AJOL, and ScienceDirect. Eligible studies included peer-reviewed AI-based diagnostic research involving skin conditions that incorporated explainability or interpretability methods. Literature published between 2015 and 2025 was screened and synthesised thematically.</p> Results <p>Evidence from studies demonstrated that deep learning models achieve high diagnostic performance in dermatology (often &gt; 85% accuracy), but consistently underperform in darker skin tones, with reported reductions of up to 20%. Explainable AI techniques such as saliency maps, Grad-CAM, and confidence scoring were shown to enhance interpretability and support differential diagnosis, though limitations related to dataset diversity and real-world deployment persist.</p> Conclusion <p>Explainable AI represents a critical advancement for equitable and reliable diagnosis of skin-manifesting NTDs. Addressing dataset bias, embedding transparency, and aligning AI tools with frontline workflows are essential to maximise clinical and public health impact.</p> Clinical trial number <p>Not applicable.</p>

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Explainable AI for differential diagnosis of skin-manifesting neglected tropical diseases (NTDS) in darker skin tones

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月3日

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  • David Chinaecherem Innocent,
  • Precious Ebube Anyakorah,
  • Rejoicing Chijindum Innocent,
  • Increase Praise Innocent,
  • Uchechukwu Madukaku Chukwuocha,
  • Ikechukwu Nosike Simplicius Dozie,
  • Chiagoziem Ogazirilem Emerole,
  • Jude Eguolo Moroh,
  • Tamunowengifiri Charles George,
  • Allen-Adebayo Blessing,
  • Ugonma Winnie Dozie,
  • Juliet Chinaza Anuwe,
  • Florence Nkemehule,
  • Ibrahima Kalil Kourouma,
  • Sophia Ifechidere Obani,
  • Ali Rabaan,
  • Ajibade Taofeeq Damileye

摘要

Background

Skin-manifesting neglected tropical diseases (NTDs) pose significant diagnostic challenges due to overlapping clinical presentations and limited access to specialist care in endemic regions. Artificial intelligence (AI) has shown promise in dermatological diagnosis; however, concerns remain regarding algorithmic bias, reduced accuracy in darker skin tones, and lack of transparency in decision-making.

Aim

This review aimed to synthesise existing evidence on explainable AI approaches for the differential diagnosis of skin-manifesting NTDs, with emphasis on performance, equity across dark skin tones, and clinical applicability.

Methods

A structured narrative review was conducted using systematic search methods across PubMed/MEDLINE, Scopus, AJOL, and ScienceDirect. Eligible studies included peer-reviewed AI-based diagnostic research involving skin conditions that incorporated explainability or interpretability methods. Literature published between 2015 and 2025 was screened and synthesised thematically.

Results

Evidence from studies demonstrated that deep learning models achieve high diagnostic performance in dermatology (often > 85% accuracy), but consistently underperform in darker skin tones, with reported reductions of up to 20%. Explainable AI techniques such as saliency maps, Grad-CAM, and confidence scoring were shown to enhance interpretability and support differential diagnosis, though limitations related to dataset diversity and real-world deployment persist.

Conclusion

Explainable AI represents a critical advancement for equitable and reliable diagnosis of skin-manifesting NTDs. Addressing dataset bias, embedding transparency, and aligning AI tools with frontline workflows are essential to maximise clinical and public health impact.

Clinical trial number

Not applicable.