  Recent research has supported that system explainability improves user trust
and willingness to use medical AI for diagnostic support. In this paper, we use
chest disease diagnosis based on X-Ray images as a case study to investigate
user trust and reliance. Building off explainability, we propose a support
system where users (radiologists) can view causal explanations for final
decisions. After observing these causal explanations, users provided their
opinions of the model predictions and could correct explanations if they did
not agree. We measured user trust as the agreement between the model's and the
radiologist's diagnosis as well as the radiologists' feedback on the model
explanations. Additionally, they reported their trust in the system. We tested
our model on the CXR-Eye dataset and it achieved an overall accuracy of 74.1%.
However, the experts in our user study agreed with the model for only 46.4% of
the cases, indicating the necessity of improving the trust. The self-reported
trust score was 3.2 on a scale of 1.0 to 5.0, showing that the users tended to
trust the model but the trust still needs to be enhanced.
