Key considerations in the adoption of Artificial Intelligence in public health
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) into public health has the potential to transform the field, influencing healthcare at the population level. AI can aid in disease surveillance, diagnosis, and treatment decisions, impacting how healthcare professionals deliver care. However, it raises critical questions about inputs, values, and biases that must be addressed to ensure its effectiveness. This article investigates the factors influencing the values guiding AI technology and the potential consequences for public health. It outlines four key considerations that should shape discussions regarding the role of AI in the future of public health. These include the potential omission of vital factors due to incomplete data inputs, the challenge of balancing trade-offs in public health decisions, managing conflicting inputs between public health objectives and community preferences, and the importance of acknowledging the values and biases embedded in AI systems, which could influence public health policy-making.
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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 | 0.172 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.030 | 0.025 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".