Comprehensive Summary
Abejirinde et al. explore how machine-learning frameworks can inform community-engagement principles in diabetes prevention and management. Following a literature review of AI applications in pathology and public health, the authors held a participatory workshop with patients, caregivers, community organizations, clinicians, and policymakers to rank guiding principles for equitable AI design. Ten principles were identified across the AI lifecycle, with trust, equity, accountability, transparency, co-design, and value alignment ranked highest. Participants emphasized that meaningful engagement demands inclusivity, diversity, and continual dialogue between communities and AI developers to ensure that health technologies reflect shared priorities rather than purely technical goals.
Outcomes and Implications
As AI becomes more central to healthcare management, particularly for chronic diseases like diabetes, community engagement remains essential to prevent bias, mistrust, and inequitable outcomes. This research demonstrates that AI models are most effective when co-designed with input from patients, caregivers, and community organizations. Clinically, it highlights that algorithms intended for prevention and management must incorporate stakeholder perspectives throughout development to ensure both efficacy and public trust.