The double-edged algorithm: A rapid review exploring the trustworthy and responsible use of generative AI in public health
Bibliographic record
Abstract
Objective: Generative artificial intelligence (genAI) technologies have rapidly evolved, offering potential to strengthen core public health functions such as health communication, surveillance, and emergency preparedness. While genAI may enhance public health outcomes by enabling tailored messaging, helping to combat misinformation, and supporting data-driven decision-making, its integration raises significant concerns about equity, privacy, and trust. Methods: This rapid review explores guiding principles for the trustworthy and responsible use of genAI in public health contexts. Following established rapid review protocols, peer-reviewed and grey literature published since 2014 were identified and analyzed thematically. Results: Ten articles met the inclusion criteria, focusing on genAI applications across various public health settings. Ten themes were generated that describe guiding principles for the trustworthy and responsible use of genAI in public health. The themes emphasize the importance of human oversight, transparency, equity, accountability, and culturally relevant communication. While genAI can be used to support health behavior change, enhance health communication across literacy levels, and promote community engagement, risks such as algorithmic bias, data misuse, and the amplification of health disinformation must be mitigated. Conclusion: Organizational policies must reflect ethical considerations and address current regulatory gaps to help mitigate these risks. Workforce training, interdisciplinary collaboration, and policy development are vital to support responsible and trustworthy implementation. This review provides preliminary insights that can help public health organizations begin to consider guidling principles for policies for guiding genAI adoption and use, emphasizing the importance of human-centered and ethically grounded approaches. Findings also identify future research needs, including the evaluation of genAI tools in diverse public health contexts, assessment of real-world impacts, and exploration of governance frameworks. This review offers an initial foundation for public health organizations to consider potential applications of genAI and develop policies that support its responsible and trustworthy use.
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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 | 0.015 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".