Commentary: Patient Perspectives on Artificial Intelligence; What have We Learned and How Should We Move Forward?

Abstract

Artificial intelligence (AI) in healthcare has now begun to make its contributions to real-world patient care with varying degrees of both public and clinical acceptability around it. The heavy investment from governments, industry and academia needed to reach this point has helped to surface different perspectives on AI. As clinical AI applications become a reality, however, there is an increasing need to harness and integrate patient perspectives, which address the distinct needs of different populations, healthcare systems and clinical problems more closely. Despite this need, patient perspectives on AI implementation have little presence in academic literature and within implementation science and are not sufficiently considered throughout the MedTech and eHealthtech product development cycle, which brings its own challenges and opportunities. This joint patient expert/clinician commentary aims to briefly summarise views on AI. It reflects upon recommendations on how stakeholders such as clinicians and Health & MedTech small and medium-sized enterprises (SMEs) can make practical usage of these views. The recommendations of the authors centre around how to work better with patients to enable both product centric and patient centric innovation and person-centred care.

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

“…Additional reporting considerations are therefore needed that are not currently covered in TRIPOD 2015. Alongside methodological advancements, considerations of fairness, 62 wider acceptance of open science practices, 63 and public and patient involvement in research and implementation of research, 64 65 an update to the TRIPOD 2015 statement is needed to capture these developments and the consequences for reporting.…”

A holistic approach to integrating patient, family, and lived experience voices in the development of the BrainHealth Databank: a digital learning health system to enable artificial intelligence in the clinic

“…This perspective article is intended to contribute to our understanding of PFLE engagement in digital health and AI and this dynamic field of practice. While there is increasing recognition that PFLE engagement is crucial to the acceptability of AI in healthcare, engagement is predominantly overlooked in the development of AI or has a limited presence in academic literature (31)(32)(33). A recent scoping review on patient and public involvement in AI and digital health in mental health (33) found that only 5 of the 144 articles identified focused on PFLE, whereas most studies used and accessed patient and public perspectives to inform a project or co-design technologies.…”

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

“…Successful implementation and adoption of AI in health care will require consideration of human factors beyond AI's predictive power and technological capabilities [29]. Patients and the public can serve as collaborators with AI in health care, either through public involvement, coproduction, co-design, or any similar methodology, and can thus shape a form of AI that addresses these concerns, a position shared by other projects [13][14][15][16][33][34][35]. In facilitating relevant PPIE activities, researchers need to be especially careful of the themes identified by this consultation: experience, empowerment, raising awareness, and acknowledging and supporting diversity in PPIE.…”