Key considerations in the adoption of Artificial Intelligence in public health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it