Using Arts-Based Methods to Involve People Living in Tower Hamlets With Multiple Long-Term Conditions in the...

Including public contributors in the development of artificial intelligence (AI) systems in healthcare research is growing, however, traditional methods of participation fail to engage people from minoritised groups. This work explores how we can utilise art-based methods to involve the perspectives of those not previously included in AI development.

We collaborated with a East London-based organisation to involve people not previously included in research to contribute to a study on multiple long-term conditions (MLTCs) and polypharmacy. Patient and public involvement and engagement (PPIE) contributors all had lived experience of MLTCs and represented a range of different ages, genders, socio-demographic backgrounds and multilingual abilities. We ran a series of six workshops that used different visual arts methods; ceramics, collage, body mapping and AI-generated images, to create research priorities and to inform AI development.

The arts-based methods served as a platform for communication which supported PPIE contributors to develop multiple research priorities, for example the impact of the lack of routine appointments on MLTCs. Through these workshops PPIE contributors also highlighted concepts that are important to consider during AI model development, such as utilising local housing data and considering bias. Visual images and art helped to facilitate different forms of communication, whilst being fun and engaging and provided a way to make abstract AI concepts more tangible whilst building AI literacy.

Arts-based methods were a useful tool to make involvement in research more accessible for under-represented communities in the development of AI tools in healthcare research. There is a need for more inclusive participatory approaches as the use of AI in healthcare and research increases.

Working with staff and interpreters from a local community-based charity, Social Action for Health, we invited 22 PPIE contributors from under-represented communities in Tower Hamlets who had no previous experience of PPIE research. PPIE contributors developed the research priorities for a large academic consortia and helped create a community art exhibition to highlight their artwork. Additionally, two experienced PPIE contributors from the wider AI-Multiply study assisted with the preparation of this manuscript.