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When using medical images for diagnosis, either by clinicians or artificialintelligence (AI) systems, it is important that the images are of high quality.When an image is of low quality, the medical exam that produced the image oftenneeds to be redone. In telemedicine, a common problem is that the quality issueis only flagged once the patient has left the clinic, meaning they must returnin order to have the exam redone. This can be especially difficult for peopleliving in remote regions, who make up a substantial portion of the patients atPortal Telemedicina, a digital healthcare organization based in Brazil. In thispaper, we report on ongoing work regarding (i) the development of an AI systemfor flagging and explaining low-quality medical images in real-time, (ii) aninterview study to understand the explanation needs of stakeholders using theAI system at OurCompany, and, (iii) a longitudinal user study design to examinethe effect of including explanations on the workflow of the technicians in ourclinics. To the best of our knowledge, this would be the first longitudinalstudy on evaluating the effects of XAI methods on end-users -- stakeholdersthat use AI systems but do not have AI-specific expertise. We welcome feedbackand suggestions on our experimental setup.

Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users