Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain

TL;DR

This study compares three explainable AI methods applied to medical image analysis, demonstrating that CIU provides more transparent, faster, and more supportive explanations for healthcare professionals than LIME and SHAP.

Contribution

The paper introduces and evaluates three explainable AI methods for medical image decision support, highlighting CIU's superior performance in transparency and speed.

Findings

CIU outperforms LIME and SHAP in supporting human decision-making.

CIU provides more transparent and understandable explanations.

CIU generates explanations more rapidly than LIME and SHAP.

Abstract

In the present paper we present the potential of Explainable Artificial Intelligence methods for decision-support in medical image analysis scenarios. With three types of explainable methods applied to the same medical image data set our aim was to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). The visual explanations were provided on in-vivo gastral images obtained from a Video capsule endoscopy (VCE), with the goal of increasing the health professionals' trust in the black box predictions. We implemented two post-hoc interpretable machine learning methods LIME and SHAP and the alternative explanation approach CIU, centered on the Contextual Value and Utility (CIU). The produced explanations were evaluated using human evaluation. We conducted three user studies based on the explanations provided by LIME, SHAP and CIU. Users from different non-medical backgrounds carried out a series of tests in the web-based survey setting and stated their experience and understanding of the given explanations. Three user groups (n=20, 20, 20) with three distinct forms of explanations were quantitatively analyzed. We have found that, as hypothesized, the CIU explainable method performed better than both LIME and SHAP methods in terms of increasing support for human decision-making as well as being more transparent and thus understandable to users. Additionally, CIU outperformed LIME and SHAP by generating explanations more rapidly. Our findings suggest that there are notable differences in human decision-making between various explanation support settings. In line with that, we present three potential explainable methods that can with future improvements in implementation be generalized on different medical data sets and can provide great decision-support for medical experts.