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

by   Ana Lucic, et al.

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.


page 3

page 7


Knowledge AI: New Medical AI Solution for Medical image Diagnosis

The implementation of medical AI has always been a problem. The effect o...

One Map Does Not Fit All: Evaluating Saliency Map Explanation on Multi-Modal Medical Images

Being able to explain the prediction to clinical end-users is a necessit...

Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?

Being able to explain the prediction to clinical end-users is a necessit...

Transcending XAI Algorithm Boundaries through End-User-Inspired Design

The boundaries of existing explainable artificial intelligence (XAI) alg...

The Medical Authority of AI: A Study of AI-enabled Consumer-facing Health Technology

Recently, consumer-facing health technologies such as Artificial Intelli...

Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study

Convolutional neural networks (CNNs) offer great machine learning perfor...

Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality

Telemedicine utilization was accelerated during the COVID-19 pandemic, a...