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

TL;DR

This paper explores developing an AI system to flag and explain low-quality medical images in real-time, aiming to improve telemedicine workflows and patient outcomes, especially in remote areas.

Contribution

It introduces an AI system for real-time quality assessment and explanation of medical images, along with a user study to evaluate its impact on clinical workflows.

Findings

Development of an AI system for real-time image quality flagging

Design of a longitudinal user study to assess explanation effects

Initial insights into stakeholder explanation needs

Abstract

When using medical images for diagnosis, either by clinicians or artificial intelligence (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 often needs to be redone. In telemedicine, a common problem is that the quality issue is only flagged once the patient has left the clinic, meaning they must return in order to have the exam redone. This can be especially difficult for people living in remote regions, who make up a substantial portion of the patients at Portal Telemedicina, a digital healthcare organization based in Brazil. In this paper, we report on ongoing work regarding (i) the development of an AI system for flagging and explaining low-quality medical images in real-time, (ii) an interview study to understand the explanation needs of stakeholders using the AI system at OurCompany, and, (iii) a longitudinal user study design to examine the effect of including explanations on the workflow of the technicians in our clinics. To the best of our knowledge, this would be the first longitudinal study on evaluating the effects of XAI methods on end-users -- stakeholders that use AI systems but do not have AI-specific expertise. We welcome feedback and suggestions on our experimental setup.