| Modigh et al. [20] | The impact of patient and public involvement (PPI) in health research versus healthcare: a scoping review of reviews. | 2021 | Review | This review compares reported impacts of patient and public involvement in health research and healthcare, highlighting differences in focus and evidence strength. |
| Dengsø et al. [21] | Patient and public involvement in Nordic healthcare research: a scoping review of contemporary practice. | 2023 | Review | This review highlights the growing integration of patient and public involvement in Nordic healthcare research, with varying methodologies and terminology. |
| Cluley et al. [22] | Mapping the role of patient and public involvement during the different stages of healthcare innovation: a scoping review. | 2022 | Review | This review highlights that PPI is concentrated in early stages and service innovations, with limited focus on later adoption and diffusion stages. |
| Zidaru et al. [23] | Ensuring patient and public involvement in the transition to AI-assisted mental health care: a systematic scoping review and agenda for design justice. | 2021 | Review | This review explores public engagement in AI-assisted mental health care, highlighting ethical challenges and opportunities for PPI throughout development stages. |
| Kelly et al. [24] | The ethical matrix as a method for involving people living with disease and the wider public (PPI) in near-term artificial intelligence research. | 2023 | Qualitative | This study developed an ethical matrix to incorporate stakeholder values into AI in radiology, emphasizing accuracy, transparency, and personal connections. |
| Hui et al. [25] | Patient and public involvement workshop to shape artificial intelligence-supported connected asthma self-management research. | 2024 | Qualitative | This study demonstrates how patient involvement shapes AI-driven asthma interventions, emphasizing co-design, usability, and considerations of health inequities, privacy, and data accuracy. |
| Hughes et al. [26] | Patient and public involvement to inform priorities and practice for research using existing healthcare data for children’s and young people’s cancers. | 2023 | Qualitative | This study highlights the need for improved communication to build trust in using healthcare data for research, particularly among young cancer patients and their carers. |
| Kuo et al. [27] | Stakeholder perspectives towards diagnostic artificial intelligence: a co-produced qualitative evidence synthesis. | 2024 | Qualitative | This review highlights stakeholder perspectives on diagnostic AI, emphasizing trust, collaboration, and the need for inclusive implementation strategies. |
| Lammons et al. [28] | Centering public perceptions on translating AI into clinical practice: patient and public involvement and engagement consultation focus group study. | 2023 | Qualitative | This study highlights the importance of early patient and public involvement in AI healthcare projects to enhance acceptance, security, and effectiveness. |
| Katirai et al. [29] | Perspectives on artificial intelligence in healthcare from a patient and public involvement panel in Japan: an exploratory study. | 2023 | Qualitative | This study explores Japanese patient and public expectations and concerns about AI in healthcare, emphasizing the need for stakeholder involvement in AI deliberation. |
| Stogiannos et al. [30] | AI implementation in the UK landscape: knowledge of AI governance, perceived challenges and opportunities, and ways forward for radiographers. | 2024 | Survey | This study highlights the need for AI training, clearer governance, and stakeholder engagement to support effective AI implementation in radiography. |
| Newton & Dimopoulos-Bick [31] | Assessing early feasibility of a novel innovation to increase consumer partnership capability within an Australian health innovation organisation using a mixed-method approach. | 2024 | Mixed | This study demonstrates the feasibility of the Partner Ring model for enhancing consumer engagement capability in healthcare organizations, showing positive acceptance and practical benefits. |
| McKay et al. [32] | Public governance of medical artificial intelligence research in the UK: an integrated multi-scale model. | 2022 | Theoretical | This paper proposes a multi-scale model integrating lay representation, PPI groups, and citizen forums to enhance public governance of medical AI research in the UK. |
| Banerjee et al. [33] | Patient and public involvement to build trust in artificial intelligence: a framework, tools, and case studies. | 2022 | Theoretical | This study proposes co-designing AI algorithms with patients and healthcare workers to enhance realistic expectations and adoption in healthcare. |
| Rogers et al. [34] | Evaluation of artificial intelligence clinical applications: detailed case analyses show value of healthcare ethics approach in identifying patient care issues. | 2021 | Theoretical | This paper analyzes the ethical implications of AI-based clinical decision support systems, emphasizing the need for context-specific ethical evaluation in healthcare applications. |
| Donia & Shaw [35] | Co-design and ethical artificial intelligence for health: an agenda for critical research and practice. | 2021 | Theoretical | This paper examines the challenges of co-designing AI/ML healthcare technologies, identifying three pitfalls and proposing solutions to address ethical concerns. |