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. 2023 Sep 26:25:e49303.
doi: 10.2196/49303.

Centering Public Perceptions on Translating AI Into Clinical Practice: Patient and Public Involvement and Engagement Consultation Focus Group Study

William Lammons #  1 , Milou Silkens #  2   3 , Jamie Hunter  4 , Sudhir Shah  5 , Charitini Stavropoulou  3
Affiliations

Centering Public Perceptions on Translating AI Into Clinical Practice: Patient and Public Involvement and Engagement Consultation Focus Group Study

William Lammons et al. J Med Internet Res. .

Abstract

Background: Artificial intelligence (AI) is widely considered to be the new technical advancement capable of a large-scale modernization of health care. Considering AI's potential impact on the clinician-patient relationship, health care provision, and health care systems more widely, patients and the wider public should be a part of the development, implementation, and embedding of AI applications in health care. Failing to establish patient and public engagement and involvement (PPIE) can limit AI's impact.

Objective: This study aims to (1) understand patients' and the public's perceived benefits and challenges for AI and (2) clarify how to best conduct PPIE in projects on translating AI into clinical practice, given public perceptions of AI.

Methods: We conducted this qualitative PPIE focus-group consultation in the United Kingdom. A total of 17 public collaborators representing 7 National Institute of Health and Care Research Applied Research Collaborations across England participated in 1 of 3 web-based semistructured focus group discussions. We explored public collaborators' understandings, experiences, and perceptions of AI applications in health care. Transcripts were coanalyzed iteratively with 2 public coauthors using thematic analysis.

Results: We identified 3 primary deductive themes with 7 corresponding inductive subthemes. Primary theme 1, advantages of implementing AI in health care, had 2 subthemes: system improvements and improve quality of patient care and shared decision-making. Primary theme 2, challenges of implementing AI in health care, had 3 subthemes: challenges with security, bias, and access; public misunderstanding of AI; and lack of human touch in care and decision-making. Primary theme 3, recommendations on PPIE for AI in health care, had 2 subthemes: experience, empowerment, and raising awareness; and acknowledging and supporting diversity in PPIE.

Conclusions: Patients and the public can bring unique perspectives on the development, implementation, and embedding of AI in health care. Early PPIE is therefore crucial not only to safeguard patients but also to increase the chances of acceptance of AI by the public and the impact AI can make in terms of outcomes.

Keywords: AI; AI in health care; acceptance; artificial intelligence; health care; health care research; patient and public engagement and involvement; patient engagement; public engagement; transition.

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Conflict of interest statement

Conflicts of Interest: None declared.

References

    1. Gilbert FJ, Smye SW, Schönlieb CB. Artificial intelligence in clinical imaging: a health system approach. Clin Radiol. 2020;75(1):3–6. doi: 10.1016/j.crad.2019.09.122. https://eprints.whiterose.ac.uk/152179/ S0009-9260(19)30542-2 - DOI - PubMed
    1. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–36. doi: 10.1038/s41591-018-0307-0. https://europepmc.org/abstract/MED/30617336 10.1038/s41591-018-0307-0 - DOI - PMC - PubMed
    1. Plana D, Shung DL, Grimshaw AA, Saraf A, Sung JJY, Kann BH. Randomized clinical trials of machine learning interventions in health care: a systematic review. JAMA Netw Open. 2022;5(9):e2233946. doi: 10.1001/jamanetworkopen.2022.33946. https://europepmc.org/abstract/MED/36173632 2796833 - DOI - PMC - PubMed
    1. van de Sande D, van Genderen ME, Huiskens J, Gommers D, van Bommel J. Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med. 2021;47(7):750–760. doi: 10.1007/s00134-021-06446-7. https://europepmc.org/abstract/MED/34089064 10.1007/s00134-021-06446-7 - DOI - PMC - PubMed
    1. Fenech M, Strukelj N, Buston O. Ethical, social, and political challenges of artificial intelligence in health. Wellcome Trust and Future Advocacy. 2018. [2023-09-06]. https://cms.wellcome.org/sites/default/files/ai-in-health-ethical-social... .

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