Explainable AI in Telemedicine: A Scoping Review of Clinical Usability Gaps and Research Directions for GP-Centred Design
TL;DR
A scoping review of XAI applications in telemedicine is presented, examining 20 studies published between 2022 and 2026 to characterise the methods used, their evaluation approaches, and gaps between technical explainability and clinical usability.
Abstract
: Remote healthcare services have grown significantly since the COVID-19 pandemic, with AI increasingly deployed in telemedicine for diagnosis support, triage, and monitoring. While Explainable AI (XAI) methods have been proposed to address trust and transparency concerns, their adoption in clinical practice remains limited — particularly in general practice settings where time constraints, diagnostic breadth, and patient-facing communication impose unique demands on explanation design. This paper presents a scoping review of XAI applications in telemedicine, examining 20 studies published between 2022 and 2026 to characterise the methods used, their evaluation approaches, and gaps between technical explainability and clinical usability. SHAP was the dominant method across the corpus, yet only three papers operated in a telemedicine context, and none evaluated XAI with GPs or in primary care. Empirical evaluation with clinical users was rare, small-scale, and methodologically inconsistent, with a median sample size of 21 participants across five empirical studies. We identify systematic gaps in telemedicine context, GP user focus, and HCI integration, and conclude with research directions for human-centred XAI in telemedicine, derived from identified gaps.