Artificial intelligence and public health: prospects, hype and challenges

Don Nutbeam; Andrew J. Milat

doi:10.1071/PU24001

RESEARCH ARTICLE (Open Access)

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Artificial intelligence and public health: prospects, hype and challenges

Don Nutbeam ^A ^B and Andrew J. Milat ^A ^B ^*

+ Author Affiliations

- Author Affiliations

^A School of Public Health, University of Sydney, Camperdown, NSW, Australia.

^B Sydney Health Partners, NSW, Australia.

^* Correspondence to: andrew.milat@sydney.edu.au

Public Health Research and Practice 35, PU24001 https://doi.org/10.1071/PU24001

Submitted: 25 June 2024 Accepted: 11 November 2024 Published: 12 March 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of the Sax Institute. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA).

Abstract

Objectives and importance of the study

Applications of artificial intelligence (AI) platforms and technologies to healthcare have been widely promoted as offering revolutionary improvements and efficiencies in clinical practice and health services organisation. Practical applications of AI in public health are now emerging and receiving similar attention. This paper provides an overview of the issues and examples of research that help separate the potential from the hype.

Methods

Selective review and analysis of cross-section of relevant literature.

Results

Great potential exists for the use of AI in public health practice and research. This includes immediate applications in improving health education and communication directly with the public, as well as great potential for the productive use of generative AI through chatbots and virtual assistants in health communication. AI also has applications in disease surveillance and public health science, for example in improving epidemic and pandemic early warning systems, in synthetic data generation, in sequential decision-making in uncertain conditions (reinforcement learning) and in disease risk prediction. Most published research examining these and other applications is at a fairly early stage, making it difficult to separate the probable benefits from the hype. This research is undoubtedly demonstrating great potential but also identifying challenges, for example in the quality and relevance of health information being produced by generative AI; in access, trust and use of the technology by different populations; and in the practical application of AI to support disease surveillance and public health science. There are real risks that current access and patterns of use may exacerbate existing inequities in health and that the orientation towards the personalisation of health advice may divert attention away from underlying social and economic determinants of health.

Conclusions

Realising the potential of AI not only requires further research and experimentation but also careful consideration of its ethical implications and thoughtful regulation. This will ensure that advances in these technologies serve the best interests of individuals and communities worldwide and don’t exacerbate existing health inequalities.

Keywords: AI, artificial intelligence, chatbots, ChatGPT, communication, generative AI, healthcare, health education, machine learning, public health, reinforcement learning.

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