JMIR Publications

ABSTRACT

Shared decision-making is central to patient-centered care but is often hampered by AI systems that focus on technical transparency rather than delivering context-rich, clinically meaningful reasoning. Although XAI methods elucidate how decisions are made, they fall short in addressing the “why” that supports effective patient–clinician dialogue. To bridge this gap, we introduce AI-SDM, a conceptual framework designed to integrate AI-based reasoning into Shared decision-making to enhance care quality while preserving patient autonomy. AI-SDM is a structured, multi-model framework that synthesizes predictive modelling, evidence-based recommendations, and generative AI techniques to produce adaptive, context-sensitive explanations. The framework distinguishes conventional AI explainability from AI reasoning—prioritizing the generation of tailored, narrative justifications that inform shared decisions. A hypothetical clinical scenario in stroke management is used to illustrate how AI-SDM facilitates an iterative, triadic deliberation process between healthcare providers, patients, and AI outputs. This integration is intended to transform raw algorithmic data into actionable insights that directly support the decision-making process without supplanting human judgment.