Using AI-Supported Supervision in a University Telemental Health Training Clinic

Abstract

Artificial Intelligence (AI) technologies have the potential of transforming clinical education and supervision in university-based telemental health training clinics. AI can improve the accuracy of diagnoses, automate routine tasks, and personalize treatment plans, potentially enhancing the accessibility and quality of mental health care. In this paper, we describe why training clinics serve as an optimal setting to adopt innovation and share lessons from the field to inform future integrations of AI in clinical supervision. The lessons include support for case conceptualization, feedback on session quality, and automation of routine tasks such as sending standardized assessments and writing progress notes. However, implementing new technology requires careful consideration of ethical and practical issues such as data privacy, algorithmic bias, and transparency. AI-supported supervision can provide valuable support for clinical training, but adequate training and education are necessary for successful integration.

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