AI for Enhancing Psychiatric Outcomes in Clinical Practice

The study assessed the effectiveness of an AI-enabled platform called Xaant (pronounced as ‘Shaant’) for automatically identifying Generalized Anxiety Disorder (GAD), Depressive Disorder, Mixed State (co-morbid anxiety and depressed state) and the Neutral State in a clinical setting. By utilizing non-invasive sensors to monitor the Autonomic Nervous System (ANS), the Xaant Platform analyzed 15-minute rest-state data from 170 participants, generating comprehensive mental health reports, predicting the subjects’ Current Medical Condition (CMC). The platform’s diagnostic outcomes were compared with those of Principal Investigators (PIs) and the subjects’ self-reported assessments. An agreement of 91.23% between the platform’s and PIs findings were recorded, with a strong inter-rater reliability (κ = 0.838). Significant differences in physiological markers between the control and the subject groups provided objective support for the platform’s diagnostic abilities. The results indicate that AI tools like Xaant can serve as a valuable complement to traditional clinical assessments, potentially enhancing diagnostic accuracy and improving patient outcomes in clinical practice.