Designing AI-Enabled Video Monitoring Clinician Dashboard for Neuropsychiatric Symptoms: A Survey of User Needs

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Authors: Christine E. Gould, Carter H. Davis, Narayan Schütz, F Vankee Lin, Quincy Miles Samus, Tracy Terada, Merryn Daniel, Silvia Tee, Ehsan Adeli
Cited by: 0
References: 6

Abstract

Objective: This study aimed to gather input from clinicians who assess and treat neuropsychiatric symptoms (NPS) to inform the development of a clinician dashboard to accompany an AI-enabled video-based monitoring system. Methods: n Results: The most important NPS to be detected were agitation/aggression, nighttime behaviors, depression, and anxiety. Multiple environmental factors were endorsed as being very important including: behavior frequency, intensity, and time of day. Conclusions: Findings demonstrate that the desired features of the dashboard were consistent across both prescribing and nonprescribing clinicians. Notably, some of the important symptoms and features that clinicians desired in a dashboard could not be extracted from existing sensor-based systems, but would be possible with an AI-enabled video monitoring system.

Fields of Study & Subjects

Digital Mental Health Interventions Artificial Intelligence in Healthcare and Education Emotion and Mood Recognition

Publication details

Journal / source
The American Journal of Geriatric Psychiatry Open Science Education and Practice
Volume
9
Pages
46–51
Publication date
10 January 2026
Language
EN