Syversen, A., Umney, O., Howell, L. et al. (8 more authors) (2025) “How can we involve Patients?” - Students’ perspectives on embedding PPIE into a doctoral training centre for AI in medical diagnosis and care. Research Involvement and Engagement, 11. 77. ISSN: 2056-7529
Abstract
Artificial intelligence (AI) in healthcare is a rapidly developing research field, but there is limited evidence that patients and public are widely engaged or involved with its progression. Alongside this, there is a growing recognition of the importance of incorporating Patient and Public Involvement and Engagement (PPIE) earlier into researcher training. Doctoral training programmes (centres) may provide the perfect environment to address both issues. This paper describes and evaluates Patient and Public Involvement and Engagement (PPIE) activities within the Centre for Doctoral Training (CDT) in Artificial Intelligence for Medical Diagnosis and Care (“AI-Medical”), at the University of Leeds in the United Kingdom. Authored primarily by PhD candidates from the AI-Medical CDT, it gives an overview of the PPIE activities conducted within the CDT, including accounts of first-hand experiences, supported by quotes and reflections from students. It also shares key learning outcomes and makes actionable recommendations for integrating PPIE into future PhD programmes and individual research projects. These insights highlight both the successes and challenges of embedding PPIE in healthcare-focused AI research projects in a doctoral training centre.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2025. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Patient and public involvement and engagement, Doctoral training programmes, Artificial intelligence, Healthcare, PhD students, Junior researchers |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Jun 2025 10:04 |
Last Modified: | 06 Aug 2025 12:46 |
Status: | Published online |
Publisher: | BMC |
Identification Number: | 10.1186/s40900-025-00750-y |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:228359 |