Two-Eyed Seeing and artificial intelligence: Enhancing healthcare delivery in Indigenous communities requires an ethical and culturally relevant public health framework
Bibliographic record
Abstract
Artificial intelligence (AI) is poised to transform healthcare delivery; this may be particularly important to underserved rural, remote, and Indigenous communities. This commentary explores the potential of AI to enhance healthcare access and outcomes of these populations while emphasizing the need for culturally safe and ethical implementation. By integrating AI with Indigenous knowledge systems through the Two-Eyed Seeing approach, we propose a framework that ensures that AI-driven healthcare is equitable, culturally sensitive, and effective. This public health perspective highlights the importance of approaching AI advancements with a culturally appropriate and relevant lens.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".