Conversational AI Should Fill the White Space in Mental Health Care, Not Replace Humans
Year: 2026
Type: preprint
Abstract: Conversational AI (CAI) is being rapidly deployed in mental healthcare settings, with much of the attention focused on using chatbots to deliver therapy directly to patients. However, we argue that this vision is too narrow and overlooks broader opportunities for CAI to extend existing systems of care. Mental health care systems have struggled for decades with structural gaps outside of immediate therapy delivery. Specifically, we identify opportunities for CAI to support (1) prevention and screening, (2) patients on waitlists, (3) between-session processes during ongoing therapy, and (4) post-treatment aftercare. Patients spend most of their time in these stages where human support is currently the least available. Rather than focusing on replacing clinicians in therapy, we argue that CAI should be strategically integrated into the broader care infrastructure to make the care system more seamless, less burdensome, and increase its capacity, thereby enhancing its effectiveness. We review preliminary studies that suggest that this is plausible. Across stages of healthcare delivery, we highlight specific opportunities and candidate mechanisms for effective and ethical implementation of such blended human-AI care.
Language: English
Subfield: Applied Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7197021208