Patient Perceptions of Artificial Intelligence in Diabetes Self-Management: Cross-Sectional Survey Study.

Background: Artificial intelligence (AI) is increasingly applied in chronic disease management, including diabetes, where it has the potential to support real-time data interpretation, improve clinical decision-making, and enhance patient engagement. Although AI tools are often developed to increase efficiency and personalization, there is limited evidence on how patients perceive the role of AI in managing their condition, particularly in relation to shared decision-making (SDM) and the patient-provider relationship. Objective: This study explored how people with diabetes perceive the usefulness of AI across key self-management tasks and examined their preferences for AI versus health care provider (HCP) involvement. It also assessed predictors of AI preference and proposed a conceptual foundation for integrating AI into a triadic SDM model involving patients, HCPs, and AI. Methods: We conducted a cross-sectional online survey of adults with diabetes in New Zealand. Participants were asked to rate 7 diabetes self-management tasks in terms of (1) current HCP involvement, (2) perceived usefulness of AI, (3) comfort with HCPs using AI, and (4) preference for AI, HCP, or both in completing each task. Tasks included data collection, data interpretation, medication adherence, treatment decision-making, lifestyle management, personal reflection, and evaluation of treatment options. Both ordinary least squares regression and ordinal logistic regression (proportional odds models) were used to identify predictors of AI preference. Results: A total of 48 participants completed the survey. Of these participants, 38 (79%) were female, 27 (56%) were aged 26 to 45 years, and 26 (54%) had higher education. Mean HCP involvement across tasks was 2.82 (SD 1.23; range 1-5). AI was viewed as moderately useful overall (mean 3.67, SD 1.20), with highest usefulness for tracking (mean 4.23, SD 1.06) and interpreting information (mean 4.40, SD 0.87). Actual AI use was reported by 15/48 (31%) participants. Participants preferred HCP involvement for tasks involving treatment decision-making (17/48, 35% vs 9/48, 19%) and personal reflection (23/48, 48% vs 9/48, 19%). Across regression models, perceived usefulness of AI was a significant predictor of preference for AI in 4 tasks: data collection (P=.02), data interpretation (P=.005), treatment decision-making (P=.04), and lifestyle management (P=.046). The patient-HCP relationship significantly predicted lower preference for AI in treatment decision-making (P=.03) and medication adherence (ordinary least squares P=.005). Comfort with HCPs using AI was generally nonsignificant. Effects were modest (adjusted R²=0.08-0.21). Conclusions: Patients demonstrated task-specific openness to AI involvement in diabetes management, particularly for structured, data-intensive activities. These findings provide a foundation for future development and evaluation of AI-integrated SDM models. Broader exploration of technology types, relationship dynamics, and collaborative decision-making will be essential as AI becomes increasingly embedded in chronic care management.