Artificial Intelligence in migrant health: a critical perspective on opportunities and risks
Bibliographic record
Abstract
Rapid advances in Artificial Intelligence (AI) are leading to the proliferation of health applications. AI presents both opportunities and risks for migrants, including refugees, and asylum-seekers. This Personal View provides a critical perspective on opportunities and risks of using AI in migrant health. It synthesises literature insights to highlight the potential health benefits of AI, for both the general population and migrants, in areas including information retrieval, translation, education, empowerment, disease prevention and diagnosis, and personalised treatments. It addresses risks posed by AI, including the potential for tracking and monitoring individuals, which could threaten the anonymity and freedom of those using digital services, as well as the perpetuation or exacerbation of biases in the algorithms used. Current deficiencies in AI, including issues of quality and tendencies to sometimes invent data, as well as to reinforce existing biases and discriminatory processes, may also adversely impact on various groups of migrants coming from different parts of the world, compounding existing ethical challenges. Given the high level of digital infrastructure and opportunities for coherent policy-making and regulatory control within the region, Europe can provide leadership in developing guidelines, policies and agreements ensuring that AI serves migrants' health needs while not compromising their rights.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".