ArticleBMJ global health2025
Responsible artificial intelligence in public health: a Delphi study on risk communication, community engagement and infodemic management.
Article in BMJ global health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial intelligence needs better health systems to reduce inequalities.NPJ digital medicine · 2026Review
- Artificial Intelligence for Weight Management in Children: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Development of research ethics guidelines for healthcare generative artificial intelligence: deriving expert consensus through a Delphi study.BMC medical ethics · 2026Article
- Public Health Responsible AI Capability (PH-RAIC) Framework: A Conceptual Model for Integrating AI into Public Health Agencies.Healthcare (Basel, Switzerland) · 2026Article
- AI-enabled proactive health education: perceptions of public health students - a qualitative study.BMC medical education · 2026Article
- Risk Perception and Communication, Plugged-In: Fifty Years of Process.Risk analysis : an official publication of the Society for Risk Analysis · 2026Article
- Developing public health risk messages about antibiotic resistance using metaphors: an international co-design and e-Delphi consensus study.Scientific reports · 2026Article
- Rethinking preventive medicine education after COVID-19: bridging training gaps in public health practice.Frontiers in medicine · 2026Review
- Public Attention to Mpox in China During the Pandemic: Qualitative Analysis of TikTok Data Using Latent Dirichlet Allocation Topic Modeling.Journal of medical Internet research · 2025Article
- Artificial intelligence in healthcare: rethinking doctor-patient relationship in megacities.Frontiers in health services · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
introductionArtificial intelligence (AI) holds the potential to fundamentally transform how public health authorities use risk communication, community engagement and infodemic management (RCCE-IM) to prepare for, manage and mitigate public health emergencies. As research on this crucial transformation remains limited, we conducted a modified Delphi study on the impact of AI on RCCE-IM.
methodsIn two successive surveys, 54 experts-scholars with expertise in public health, digital health, health communication, risk communication and AI, as well as RCCE-IM professionals-from 27 countries assessed opportunities, challenges and risks of AI, anticipated future scenarios, and identified principles and actions to facilitate the responsible use of AI. The first Delphi round followed an open, exploratory approach, while the second sought to prioritise and rank key findings from the initial phase. Qualitative thematic analysis and statistical methods were applied to evaluate responses.
resultsAccording to the expert panel, AI could be highly beneficial, particularly for risk communication (eg, tailoring messages) and infodemic management (eg, social listening), while its utility for fostering community engagement was viewed more critically. Challenges and risks affect all three components of RCCE-IM equally, with algorithmic bias and privacy breaches being of particular concern. Panellists anticipated both optimistic (eg, democratisation of information) and pessimistic (eg, erosion of public trust) future scenarios. They identified seven principles for the responsible use of AI for public health practices, with equity and transparency being the most important. Prioritised actions ranged from regulatory measures, resource allocation and feedback loops to capacity building, public trust initiatives and educational training.
conclusionTo responsibly navigate the multifaceted opportunities, challenges and risks of AI for RCCE-IM in public health emergencies, clear guiding principles, ongoing critical evaluation and training as well as societal collaboration across countries are needed.
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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.