Towards inclusive explainable artificial intelligence: a thematic analysis and scoping review on tools for persons with disabilities

Abstract

Findings reveal a strong concentration on neurological conditions - such as Alzheimer's disease, autism spectrum disorder and Parkinson's disease - with limited focus on orthopaedic, sensory and spinal impairments. SHAP was the most common explanation model, followed by LIME, LRP-B and Grad-CAM. Accessibility goals centred around clinical transparency, user comprehension, sensory/cognitive adaptation and trust in low-resource settings. Thematic analysis identified three overarching dimensions: modelling techniques, decision-making and trust and diverse application contexts. Expanding XAI to underrepresented impairments and embedding multimodal, user-centred explanations into rehabilitation workflows - through participatory design, ethical oversight and standardised evaluation - can enhance autonomy, improve personalisation and support more effective, equitable care.

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