Mind the (widening) gap: why public health must engage with AI now.
This commentary aims to highlight the opportunities and challenges that Artificial Intelligence (AI) presents for public health. Narrative commentary and conceptual analysis. The commentary draws on material developed for a forthcoming book by the European Observatory on Health Systems and Policies....
| Publicado en: | Public Health (Elsevier) Vol. 250 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190851795&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190851795 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00333506 18J0 jtl: Public Health (Elsevier) issn: 00333506 maglogo: N pubinfo: dt: Jan2026 vid: 250 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 190851795 10.1016/j.puhe.2025.106047 190851795 ppct: 1 formats: tig: atl: Mind the (widening) gap: why public health must engage with AI now. aug: au: del Rey Puech, Paula Payne, Rebecca Saund, Jasjot McKee, Martin affil: Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, United Kingdom sug: subj: Public Health Trends Artificial Intelligence Utilization Technology Trends Health Inequities Narratives Concept Formation Social Determinants of Health Systems Theory Behavioral Changes Communication Disease Surveillance Workforce Information Systems Social Participation Resource Allocation Implementation Science ab: This commentary aims to highlight the opportunities and challenges that Artificial Intelligence (AI) presents for public health. Narrative commentary and conceptual analysis. The commentary draws on material developed for a forthcoming book by the European Observatory on Health Systems and Policies. Sources were selected to highlight both the potential and the limitations of AI integration at population levels, with a focus on equity, governance, and implementation. The analysis is informed by established public health principles: prevention, systems thinking, and the social determinants of health. AI applications in public health go beyond process automation and operational efficiency. By integrating and processing diverse, multi-modal data sources, its implementation presents opportunities to understand the wider determinants of health at a more nuanced level and identify populations at risk with greater precision. Additionally, AI has the potential to help understand and support behaviour change in sophisticated ways, enhance disease surveillance and modelling, and enable more targeted and responsive public communication and engagement strategies. However, there are several barriers to realise AI's potential in public health, including system fragmentation, data access limitations, resource constraints, implementation challenges, workforce readiness gaps, and technological limitations such as bias and generative AI "hallucinations". Without deliberate engagement, AI risks reinforcing existing inequities. Practical steps for action include embedding AI training in public health education, building multidisciplinary teams, investing in data infrastructure, and ensuring participatory approaches. AI will continue to shape public health systems, whether or not public health professionals engage. We argue that the public health community is both uniquely positioned and ethically obligated to engage proactively with AI. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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