Perceptions and needs of artificial intelligence in healthcare to increase adoption: A scoping review


Background: Artificial intelligence (AI) provides boundless potential for improving the efficiency and effectiveness of healthcare service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts in promoting AI adoption in healthcare. Objective: This study aims to provide an overview of the perceptions and needs in AI to increase its adoption in healthcare. Methods: A systematic scoping review was conducted according to Arksey and O’Malley’s five-stage framework. Articles that described the perceptions and needs of AI in healthcare were searched across nine databases – ACM Library, CINAHL, Cochrane–Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus and Web of Science for studies that were published from inception till 21 June 2021. Articles that were not specific to AI, not research studies and not written in English were omitted. Results: A total of 3,666 articles were retrieved of which 26 articles were eligible and included in this review. Mean age ranged from 30-72.6 years old, proportion of males ranged from 0 to 73.4% and the sample sizes for studies with human subject responses ranged from 11 to 2,780. The perceptions and needs of various populations in the use of AI were identified for different healthcare use cases including general, primary and community healthcare, chronic diseases self-management, self-diagnosis, mental health and diagnostics. The use of AI was perceived to be positive due to its (1) availability and ease of use and the (2) potential for improving efficiency and reducing the cost of healthcare service delivery. However, concerns were raised regarding the (1) lack of trust in data privacy, patient safety and technological maturity and the (2) possibility of full automation. Suggestions for improving adoption of AI in healthcare were highlighted – (1) enhance personalization and customizability, (2) enhance empathy and personification of AI-enabled chatbots and avatars, (3) enhance user experience, design, and interconnectedness with other devices and (4) educate the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study. Conclusions: The perceptions and needs of AI in its use in healthcare are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in healthcare. This would facilitate the increase in effectiveness and efficiency of healthcare service delivery to improve patient outcomes and satisfaction. Clinical Trial: NA

Journal of Medical Internet Research