Principles and Practices of Community Engagement in AI for Population Health: Formative Qualitative Study of the AI for Diabetes Prediction and Prevention Project

Abstract

Abstract Background Preventing diabetes is a priority for governments and health systems worldwide. Artificial intelligence (AI) has the potential to inform prevention and planning. However, there is little guidance on how patients, caregivers, and communities are engaged in AI life cycle stages. Objective This formative qualitative study aimed to identify principles for meaningful community engagement. The goal was to support the responsible use of machine learning models in diabetes prevention and management. Methods We conducted a literature scan on how AI or digital health initiatives have engaged patients and communities. A participatory workshop was then organized with patients, caregivers, community organizations, clinicians, and policymakers. In the workshop, we identified and ranked guiding principles for community engagement in AI for population health. We also outlined key considerations for implementing these principles. Results We identified 10 principles for patient and community engagement in AI for health care from 6 papers and developed a conceptual framework for community engagement on AI. A total of 30 workshop participants discussed and ranked the top 6 principles: trust, equity, accountability, transparency, codesign, and value alignment. Participants noted that embedding community engagement in the AI life cycle requires inclusivity and diversity. Additionally, implementers should leverage existing resources and adopt a centralized approach to AI decision-making. Conclusions Our study offers useful insights for community-focused AI deployment that centers the values of patients and communities. The identified principles can guide meaningful engagement on the use of AI in health systems, while future research can operationalize the conceptual framework.