03098nas a2200421 4500000000100000008004100001260005300042653003900095653002800134653001900162653002700181653002500208653002000233653004100253100001600294700001700310700001600327700001600343700001800359700001400377700001500391700001300406700001400419700001500433700001300448700001300461700001600474700001600490700001300506700001300519700001600532245012200548856007300670300001100743490000600754520190200760022001402662 2026 d c08/2026bSpringer Science and Business Media LLC10aNeglected tropical diseases (NTDs)10aArtificial Intelligence10askin of colour10aDifferential diagnosis10aGlobal health equity10a Dermatology AI10a Explainable artificial intelligence1 aInnocent DC1 aAnyakorah PE1 aInnocent RC1 aInnocent IP1 aChukwuocha UM1 aDozie INS1 aEmerole CO1 aMoroh JE1 aGeorge TC1 aBlessing A1 aDozie UW1 aAnuwe JC1 aNkemehule F1 aKourouma IK1 aObani SI1 aRabaan A1 aDamileye AT00aExplainable AI for differential diagnosis of skin-manifesting neglected tropical diseases (NTDS) in darker skin tones uhttps://link.springer.com/content/pdf/10.1186/s44398-026-00033-w.pdf a1 - 110 v23 a

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.

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