ReviewDigital health
Generative artificial intelligence in public health research and scientific communication: A narrative review of real applications and future directions.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Building health-literate artificial intelligence.Nature human behaviour · 2026Review
- Generative artificial intelligence-assisted medical self-care and associated factors among undergraduate students at Arsi University, South-Eastern Ethiopia.PLOS digital health · 2026Article
- Artificial intelligence (AI) in action: a cross-continental survey of AI adoption among field epidemiology fellows in Canada, the United States, and Europe, 2024.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2026Article
- AI-Enabled Flexible Sensing Ecosystems for Parkinson's Disease: Advancing Digital Biomarkers and Closed-Loop Interventions.Sensors (Basel, Switzerland) · 2026Review
- Can large language models be trusted? Reliability and readability of responses to perinatal depression FAQs.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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Generative artificial intelligence (GenAI) tools such as ChatGPT-4o and Gemini are rapidly influencing public health research and communication. Their capacity to assist with drafting, summarising, and translating content offers significant potential, particularly in multilingual and resource-limited settings.This narrative review critically explored the adoption of GenAI tools in public health research and communication, focusing on their practical applications and ethical implications. Methods: This narrative review synthesised 18 recent peer-reviewed and grey literature (2023-2025) to explore the role of GenAI in public health research and communication. A hybrid human-AI approach was used, where colour-coded manual coding was combined with AI-supported thematic analysis. All AI-generated outputs were critically reviewed, verified, and refined by the author. Results: Five key themes were identified: (1) Supporting scientific research writing tasks; (2) enhancing language clarity and scientific tone; (3) bridging the gap between science and the public; (4) ethical concerns and quality assurance; and (5) future potential and the need for upskilling. Discussion: GenAI can democratise and accelerate public health research publication and communication, provided it is used transparently and critically. Human oversight and contextual judgement remain essential to ensure responsible use. Conclusion: With thoughtful implementation, GenAI can enhance human expertise in the realm of public health, academia and scientific communication. It offers an emerging opportunity to strengthen public health research and communication, particularly when supported by ethical guidelines, training, and institutional leadership.
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.