Artificial Intelligence Applications in Health Care Practice: A Scoping Review (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Artificial Intelligence (AI) is often heralded as a potential disruptor that will transform the way we do medicine. The amount of data collected and available in health care, coupled with advances in computational power, have contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff and society will not be realized if AI implementation is not better understood. </sec> <sec> <title>OBJECTIVE</title> The aim of this study was to explore how the implementation of AI in healthcare practice has been described and researched in the literature by answering three questions: 1. What are the characteristics of research on implementation of AI in practice? 2. What types and applications of AI systems are described? 3. What characteristics of the implementation process for the AI systems are discernable? </sec> <sec> <title>METHODS</title> A scoping review was conducted of Medline (PubMed), Scopus, Web of Science, CINAHL, and PsychInfo databases to identify empirical studies of AI implementation in healthcare since 2011 in addition to snowball sampling of selected reference lists. Titles and abstracts were screened and full-text articles using Rayyan.ai software. Data from the included articles was charted and summarized. </sec> <sec> <title>RESULTS</title> Of the 9218 records retrieved, forty-five articles were included. Most articles were published recently, from high-income countries, cover diverse clinical settings and disciplines, and intended for care-providers. AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. Most possess no action autonomy, but rather support human decision-making. The focus of most research is on establishing the effectiveness of interventions, or related to technical and computational aspects of AI systems. Focus on the specifics of implementation processes does not yet seem to be a priority in research and the use of frameworks to guide implementation is rare. </sec> <sec> <title>CONCLUSIONS</title> Our current empirically knowledge derives from implementations of AI systems with low action autonomy and approaches common to other types of information systems. To develop a specific and empirically-based implementation framework, further research is needed on the more disruptive types of AI systems being implemented into routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions and addressing ethical concerns around privacy and data protection. </sec>

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