Principles and Practices of Community Engagement in AI for Population Health: Formative Qualitative Study of the AI for Diabetes Prediction and Prevention Project
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".