ReviewDigital health
The double-edged algorithm: A rapid review exploring the trustworthy and responsible use of generative AI in public health.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Beyond explainability: toward trustworthy governance of generative AI in digital public health surveillance.Frontiers in public health · 2026Article
- Digital art as a novel medium for health communication: enabling interactive interventions and reconstructing health experiences in public health.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.