Stakeholder perspectives towards diagnostic artificial intelligence: a co-produced qualitative evidence synthesis

Rachel Yi Ling Kuo; Alexander Freethy; Judi Smith; Rosie Hill; Joanna C; Derek Jerome; Eli Harriss; Gary S Collins; Elizabeth Tutton; Dominic Furniss

doi:10.1016/j.eclinm.2024.102555

Stakeholder perspectives towards diagnostic artificial intelligence: a co-produced qualitative evidence synthesis

EClinicalMedicine. 2024 Mar 22:71:102555. doi: 10.1016/j.eclinm.2024.102555. eCollection 2024 May.

Authors

Affiliations

¹ Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford, UK.
² Department of Plastic Surgery, Royal Devon and Exeter Hospital, Royal Devon University Healthcare NHS Foundation Trust, UK.
³ Bodleian Health Care Libraries, Oxford, UK.
⁴ Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford, UK.

Abstract

Background: Diagnosis is a cornerstone of medical practice. Worldwide, there is increased demand for diagnostic services, exacerbating workforce shortages. Artificial intelligence (AI) technologies may improve diagnostic efficiency, accuracy, and access. Understanding stakeholder perspectives is key to informing implementation of complex interventions. We systematically reviewed the literature on stakeholder perspectives on diagnostic AI, including all English-language peer-reviewed primary qualitative or mixed-methods research.

Methods: We searched PubMed, Ovid MEDLINE/Embase, Scopus, CINAHL and Web of Science (22/2/2023 and updated 8/2/2024). The Critical Appraisal Skills Programme Checklist informed critical appraisal. We used a 'best-fit' framework approach for analysis, using the Non-adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. This study was pre-registered (PROSPERO CRD42022313782).

Findings: We screened 16,577 articles and included 44. 689 participants were interviewed, and 402 participated in focus groups. Four stakeholder groups were described: patients, clinicians, researchers and healthcare leaders. We found an under-representation of patients, researchers and leaders across articles. We summarise the differences and relationships between each group in a conceptual model, hinging on the establishment of trust, engagement and collaboration. We present a modification of the NASSS framework, tailored to diagnostic AI.

Interpretation: We provide guidance for future research and implementation of diagnostic AI, highlighting the importance of representing all stakeholder groups. We suggest that implementation strategies consider how any proposed software fits within the extended NASSS-AI framework, and how stakeholder priorities and concerns have been addressed.

Funding: RK is supported by an NIHR Doctoral Research Fellowship grant (NIHR302562), which funded patient and public involvement activities, and access to Covidence.

Keywords: AI; Artificial intelligence; Patient and public involvement; Qualitative; Qualitative evidence synthesis; Systematic review.

Publication types

Review