A Responsible AI Readiness Framework for Digital Self-Management Platforms: Illustrative Application to a Rule-Based GERD Platform

Background/Objectives: Artificial intelligence (AI) is increasingly proposed for digital self-management, yet early-stage platforms may not be clinically or organizationally ready for AI-enabled functions. Most responsible-AI guidance begins after a model has been proposed and gives limited attention to whether AI should be introduced at all. This Perspective proposes a Responsible AI Readiness Framework for digital self-management platforms. Methods: The framework was developed through a targeted integrative synthesis, concept extraction, domain consolidation, and comparison with established digital-health and AI frameworks. It was applied qualitatively, without scoring, to Sokcare, a rule-based mobile platform for gastroesophageal reflux symptoms. No participant-level data were analyzed. Conceptual Findings: The framework begins with an AI necessity and proportionality screen and then examines eight readiness domains. In the Sokcare audit, 18 of 20 fixed mission statements were classified as Revise and 2 as Retain; none were classified as Remove. A non-version-linked interface design record also contained legacy symptom-improvement and personalization wording that exceeded the intended self-management claim boundary. The case otherwise showed potential foundations in interpretability, user agency, and modular architecture, alongside gaps in clinical validation, safety escalation, cybersecurity, accessibility, implementation, and lifecycle monitoring. Conclusions: The framework may support structured pre-adoption AI decisions, but its transferability and decision consistency require testing across independent platforms, clinical areas, and regulatory settings.