A Relational Framework for Artificial Intelligence-Assisted Shared Decision Making

Background: Artificial intelligence (AI) can support clinical decision making, but accuracy, usability, and efficiency do not establish whether patients understand options or influence the agreed plan. This study aimed to develop observable relational requirements for AI-assisted shared decision making. Methods: We conducted a qualitative framework synthesis of 21 clinical, empirical, conceptual, and governance sources identified through structured purposive sampling completed on 20 September 2026. Two-pass coding combined established shared decision-making categories with inductive analysis of AI-related relational effects. Domains were retained when supported across source types, conceptually distinct, observable at the point of care, and linked to a clinical or organizational response. Results: The synthesis identified a patient–clinician–AI interaction embedded in organizational governance and highlighted five relational concerns: apparent authority of AI output, insufficient verification and contestability, substitution of inferred for expressed preferences, uncertainty without actionable follow-up, and fragmented responsibility. These concerns informed five domains: role and influence disclosure; independent verification and contestability; direct preference elicitation; actionable uncertainty and revisability; and distributed accountability and continuity. Each domain was translated into a relational question, a clinical test, and a documentation or governance response. A complementary measurement framework distinguished technical performance from decision process, relational quality, equity, safety and continuity, and governance outcomes. Conclusions: The study’s contribution is an operational framework connecting AI-related relational concerns to observable clinical requirements and candidate evaluation measures. The proposed framework requires stakeholder and prospective clinical validation; it does not establish clinical effectiveness.