Recommendations on the use of artificial intelligence in health promotion
Bibliographic record
Abstract
The purpose of this perspective is to provide recommendations on the use of Artificial Intelligence (AI) in health promotion. To arrive at these recommendations, we followed a 6-step process. The first step was to recruit an international authorship team from the Healthy Living for Pandemic Event Protection (HL- PIVOT) network. This enabled us to achieve an international perspective with insights from Canada, Great Britain, Kenya, Italy, and the US. A philosophical inquiry was conducted addressing 5 questions. What should the relationship be between humans and AI in health promotion? How can the public and professionals trust AI? How can we ensure AI is aligned with our values? How can we ensure the ethical use of data by AI? How can we control AI? 4 hypothetical scenarios were also developed to provide perspectives on: i) Artificial 'Versus' Human Intelligence; ii) AI Empowerment in Self-Care; iii) Could AI Improve Patient Provider Relationship; and iii) The Kenyan Cancer Patient at the Height of a Pandemic. Based on the philosophical inquiry and the scenarios 11 recommendations are made by the HL-PIVOT on the use of AI in health promotion. The golden thread running through these recommendations is a human centric approach. The recommendations begin by suggesting that workforce planning should take account of AI. They conclude with the statement that any serious incidents involving an AI in Health Promotion should be reported to the relevant regulatory authority.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| grok | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| opus | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".