Exploring the potential of artificial intelligence usage in the knowledge and evidence services of a public health body: A working group approach
The UK Health Security Agency’s Knowledge and Library Services established an artificial intelligence working group in 2022 to explore the potential applications of artificial intelligence of relevance to its function. This article describes the working group's initial approach to testing and evaluating artificial intelligence and machine-learning-assisted tools for information retrieval and evidence review processes. These include duplicate-reference removal, citation searching, title and abstract screening, full text screening, data extraction and critical appraisal. These exploratory tests have demonstrated varying degrees of potential for implementation, while also contributing to broader discussions on ethical considerations, copyright and licensing issues, the transparency of artificial intelligence methodology, and evidence integrity. Reflecting the exploratory nature of this work, the testing presented in this article focused on identifying potential opportunities and challenges rather than formal validation. This overview outlines the approach taken and insights gained from navigating the evolving artificial intelligence landscape and its potential implications for knowledge, library and evidence services within a public health organisation.
Authors
- Patricia Lacey (ORCID: https://orcid.org/0000-0001-8821-3366)
- Samuel M. Thomas (ORCID: https://orcid.org/0000-0002-8846-1789)
- Charlotte Bruce
- Daphné Duval (ORCID: https://orcid.org/0000-0002-1042-2114)
- Zalaya Simmons (ORCID: https://orcid.org/0009-0005-0422-0072)
- Scott Rosenberg
- Wendy Marsh (ORCID: https://orcid.org/0000-0002-9537-1239)
Institutions
- Dorset County Hospital NHS Foundation Trust (GB)
- University Hospitals Dorset NHS Foundation Trust
Publication Details
- Journal
- IFLA Journal
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1177/03400352251342513
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.31