Stakeholder Perspectives on the Integration of AI in Diabetes Care: Systematic Review of Qualitative Studies.

Background: AI is increasingly being integrated into diabetes care, with growing evidence supporting its potential to improve clinical decision-making, risk prediction, and self-management. However, the lived experiences, expectations, and concerns of those involved in its implementation have not been adequately synthesized. Objective: This study aims to synthesize qualitative evidence on the perspectives of patients, caregivers, health care professionals (HCPs), and other stakeholders regarding the integration of AI in diabetes management. Methods: We searched MEDLINE via PubMed, Web of Science, Scopus, CINAHL, and PsycINFO from inception to February 17, 2026. Eligible studies examined the perspectives of adult patients, caregivers, health care professionals, or administrators on AI-enabled tools for diabetes management, self-management, clinical decision support, or the prevention of complications, and used qualitative methods or reported a separately analyzable qualitative component. Tools required an identifiable data-driven function for prediction, classification, recommendation, personalization, or decision support. Studies focused exclusively on image-based diagnosis, technical validation, or digital tools without an identifiable AI component were excluded. Two reviewers (HM-M and JM-A) independently screened studies and extracted data. Methodological limitations were assessed using the Joanna Briggs Institute (JBI) checklist and the Cochrane Qualitative Methodological Limitations Tool (CAMELOT). Findings were synthesized using thematic synthesis. Confidence was assessed using GRADE-CERQual. A sensitivity analysis excluded questionnaire-based qualitative evidence. Results: Fourteen studies published between 2023 and 2025 were included, representing at least 738 participants across 9 countries. Participants included individuals with type 1 or type 2 diabetes, family caregivers, doctors, nurses, specialists, administrators, and other health care staff. Studies evaluated large language models, AI-enabled mobile applications and wearables, glucose-prediction systems, and clinical decision-support tools. Exposure ranged from direct use of functioning systems to evaluation of prototypes, wireframes, and hypothetical applications. Four analytical themes and 13 subthemes were identified. Stakeholders perceived that AI could support preventive and individualized care, education, self-management, and decision-making. Concerns included accuracy, bias, privacy, accountability, increased workload, caregiver burden, loss of professional autonomy, and erosion of human-centered care. Participants emphasized explainability, intuitive design, integration with existing systems, tailored training, and continued access to human support. Eleven findings were rated as high confidence and 2 as moderate confidence. Conclusions: Although stakeholders perceived AI to be useful for diabetes care, these qualitative findings do not demonstrate clinical effectiveness, safety, or improved patient outcomes. Evidence was limited by heterogeneity in AI modalities, stakeholder groups, diabetes contexts, and technology exposure, as well as demographic imbalance, restricted reporting of researcher reflexivity, and reliance on prototype or hypothetical systems. Implementation should prioritize transparent design, clinical validation, data governance, human oversight, and tailored support, while preserving professional judgment and person-centered relationships.