AI-driven telemedicine diagnostics have enhanced patient healthcare, particularly where specialists are sparse. Advancements in edge AI allow the processing of medical imaging near the sources, reducing internet connection needs. However, deploying AI in edge devices for telemedicine presents challenges, because there is a lack of transparency in decision-making. Explainable Artificial Intelligence (XAI) is crucial for improving trust in AI diagnostics, but its integration into edge AI remains limited because of resource constraints. This research addresses this gap by identifying existing XAI methods with a systematic literature review and evaluating them to adopt into edge AI in telemedicine. This research proposes the Attention Mechanism as an adopted XAI method suitable for edge AI in telemedicine. Experimental results show an XAI process time of 4,563 ms, running locally on mobile edge devices without network connectivity. The research presents an XAI adoption for edge AI in telemedicine, enabling accessible and transparent telemedicine diagnostics.