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.
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.
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.
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.
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.
BT - BMC Artificial Intelligence DA - 08/2026 DO - 10.1186/s44398-026-00033-w IS - 1 LA - ENG M3 - Article N2 -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.
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.
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.
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.
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.
PB - Springer Science and Business Media LLC PY - 2026 SP - 1 EP - 11 T2 - BMC Artificial Intelligence TI - Explainable AI for differential diagnosis of skin-manifesting neglected tropical diseases (NTDS) in darker skin tones UR - https://link.springer.com/content/pdf/10.1186/s44398-026-00033-w.pdf VL - 2 SN - 3005-1924 ER -