"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research.

Clinical Snapshot

Key Findings

• P Value: Not applicable — qualitative study

• Effect Size: Not applicable — qualitative study; no quantitative effect sizes reported

• Primary Outcome: Three key thematic areas identified: (1) the novelty of participating in AI research for stakeholders; (2) how AI knowledge is shared and communicated across disciplines; (3) the inclusion and integration of clinician and patient stakeholder perspectives in AI development workflows

• Nnt Or Sensitivity: Not applicable — qualitative study; transferability and credibility of themes serve as the qualitative analogue of generalisability

• Confidence Interval: Not applicable — qualitative study

Clinical Application

The findings are directly actionable for research consortium design and governance. Recommendations implied by the findings — such as tailoring communication tools and information to audience, explicitly scaffolding AI literacy for clinical and patient stakeholders, and building structured knowledge-sharing mechanisms — are feasible within existing research infrastructure. No additional clinical resources or regulatory approvals are required to act on these findings. In Australia, the relevance is high and growing. The Australian Government's National AI Strategy and the MRFF-funded AI-in-health research programmes (including the Australian Health Research Alliance) are creating similar interdisciplinary consortia to those studied here. The RACGP has highlighted the need for clinician engagement in AI tool development to ensure clinical validity and safety. The TGA's evolving regulatory framework for Software as a Medical Device (SaMD) increasingly requires multidisciplinary input including clinical and consumer perspectives. The findings regarding communication barriers between data scientists and clinicians are directly applicable to Australian primary care and hospital-based AI implementation contexts. Aboriginal and Torres Strait Islander health data sovereignty considerations add an additional layer of stakeholder complexity not addressed in this UK-focused study. Healthcare professionals, data scientists, health informaticians, and patient representatives participating in or designing interdisciplinary AI-in-healthcare research programmes, consortia, or implementation projects

Abstract

Developing artificial intelligence (AI) algorithms for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other stakeholders together. By understanding AI as 'sociotechnical' where the social and the technical nature of the work and the models are inseparable, we explore the AI development workflow and how stakeholders navigate the challenges and tensions of sharing and generating knowledge across disciplines. We conducted an inductive thematic analysis of 13 semi-structured interviews with participants in early stages of AI-in-healthcare research consortia in the UK. Our findings identify that participants needed to adapt both the tools used for sharing and the information communicated according to their audience, particularly when working with those with a clinical or patient perspective. We identify the novelty of participating in AI research, how AI knowledge is shared, and the inclusion of clinician and patient stakeholder perspectives as key areas within collaborative AI practices in healthcare. These findings highlight that bringing AI into the mix can introduce new obstacles to interdisciplinary work.

References

1. 1.Henkin, R., Remfry, E., Reynolds, D. J., Clinch, M., & Barnes, M. R. (2026). "We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research. PLoS ONE. https://doi.org/10.1371/journal.pone.0354358