Combining AI and human support in mental health: a digital intervention with comparable effectiveness to human-delivered care

Abstract

AbstractEscalating global mental health demand exceeds existing clinical capacity. Scalable digital solutions will be essential to expand access to high-quality mental healthcare. This study evaluated the effectiveness of a digital intervention to alleviate mild, moderate and severe symptoms of generalized anxiety. This structured, evidence-based program combined an Artificial Intelligence (AI) driven conversational agent to deliver content with human clinical oversight and user support to maximize engagement and effectiveness. The digital intervention was compared to three propensity-matched real-world patient comparator groups: i) waiting control; ii) face-to-face cognitive behavioral therapy (CBT); and iii) remote typed-CBT. Endpoints for effectiveness, engagement, acceptability, and safety were collected before, during and after the intervention, and at one-month follow-up. Participants (n=299) used the program for a median of 6 hours over 53 days. There was a large clinically meaningful reduction in anxiety symptoms for the intervention group (per-protocol (n=169): change on GAD-7 = −7.4,d= 1.6; intention-to-treat (n=299): change on GAD-7 = −5.4,d= 1.1) that was statistically superior to the waiting control, non-inferior to human-delivered care, and was sustained at one-month follow-up. By combining AI and human support, the digital intervention achieved clinical outcomes comparable to human-delivered care while significantly reducing the required clinician time. These findings highlight the immense potential of technology to scale effective evidence-based mental healthcare, address unmet need, and ultimately impact quality of life and economic burden globally.