The Medicalization Gap in Accessible Explainable AI

Explainable AI (XAI) promises to make AI decisions understandable to humans, but for whom? We conducted systematic search from Scopus, for comprehensive accessibility XAI and for agentic/LLM XAI with accessibility search. Analysis reveals a profound medicalization gap: 76 papers (98.7\%) use XAI to diagnose or study accessibility populations, while only one design explanations for users. The agentic search shows the same pattern: all 10 papers use XAI to study populations, with none designing accessible explanations for users of agentic/LLM systems. This reveals current research addresses clinicians' and developers' needs but overlooks disabled users' accessible explainability needs. We propose shifting from XAI \textit{on} populations to XAI \textit{for} users with concrete recommendations for inclusive needs assessment.