Ethical issues raised by artificial intelligence and big data in population health: a scoping review
Bibliographic record
Abstract
Introduction: Artificial intelligence systems (AIS) powered by big data (BD) are more and more common in the healthcare sector and many anticipate that they will have a substantial effect on population health. Facing the disruptive potential of these transformations, there is a need to keep the pace with the ethical reflection accompanying the uses of AIS and the BD systems enabling such innovations. Methods: To carry out this task, we conducted a scoping review of the ethical issues of AIS and BD, in population health, based on 243 scholarly articles. Results: Our results show the explosion of publications on the subject in recent years. Our qualitative analysis of this literature highlights the potential issues of AIS and BD on the three components of population health: (1) the health outcomes and their distribution in the population and between populations; (2) the patterns of health determinants; (3) the policies and interventions developed to connect the previous components. Discussion: Our conclusions show the uncertainty of the positive outcomes of these technologies and their potential for unequal distribution. Authors consider that AIS and BD will affect determinants of health either in their understanding and by transforming the structure of these determinants. At last, this review points that the policies and interventions developed to attain population health goals will have to answer to numerous ethical expectations. This review offers a comprehensive mapping of ethical issues raised by the uses of AIS in the global field of population health.
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.
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 | 0.030 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".