Abstract
Abstract
AI-powered mental health tools are increasingly deployed to support users across multiple sessions, yet the field lacks a principled framework for how memory in these systems should be structured and applied. In most current implementations, memory functions primarily as a personalization mechanism, optimizing for conversational continuity and user engagement without distinguishing between types of information that have fundamentally different clinical relevance. We propose a framework organizing memory in AI-powered mental health systems into 4 functionally distinct types. Episodic memory captures discrete, time-bound experiences tied to specific events and context. Pattern memory, adapted from the concept of procedural memory in cognitive psychology, tracks recurring patterns in cognition, emotion, and behavior across sessions. Semantic memory captures stable, personally relevant background context about the user. State-responsive memory represents the user’s current emotional and psychological condition in real time, taking priority over the other 3 types when acute distress or risk is signaled. Each type corresponds to a distinct therapeutically relevant function, and together they are designed to support the kind of cumulative, longitudinal understanding that effective mental health care requires. We term this framework “therapeutically informed memory,” drawing on established memory systems research and applying it to the clinical requirements of AI-powered mental health support. The aim of this viewpoint paper is to give AI developers, clinicians, and mental health organizations a shared vocabulary and design framework for organizing memory around therapeutic function rather than personalization alone. This paper is intended primarily for AI product and engineering teams, clinical advisors to digital mental health companies, and researchers evaluating AI-powered mental health tools. We describe the design requirements and clinical rationale for each memory type, discuss how the types interact and how priority should be assigned across them, and use Yuna, an AI-powered digital mental health intervention developed with clinical input, as an illustrative example of how this framework can be applied in practice. We conclude with design implications for the field and identify open questions regarding memory quality metrics, outcome validation, and the ethical dimensions of persistent memory as priorities for future research.