Media is loading


This paper presents a novel artifact called MDscan that can help mental health professionals quickly screen a large number of patients for ten mental disorders. MDscan uses patient responses to the SCL-90-R clinical questionnaire to create a full-color image, similar to radiological images, which identifies which disorder or combination of disorders may afflict a patient, the severity of the disorder, and the underlying logic of this prediction, using an explainable artificial intelligence (XAI) approach. While prior artificial intelligence (AI) tools have seen limited acceptance in clinical practice because of the lack of transparency and interpretability in their "black box" models, the XAI approach used in MDscan is a "white box" model that elaborates which patient feature contributes to the predicted outcome and to what extent. Using patient data from a mental health clinic, we demonstrate that MDscan outperforms current (expert-based) clinical practice by an average of 20%.