Artificial intelligence and machine learning in mobile apps for mental health: A scoping review
Madison Milne-Ives, Emma Selby, Becky Inkster, Ching Lam, Edward Meinert
Abstract
Mental health conditions can have significant negative impacts on wellbeing and healthcare systems. Despite their high prevalence worldwide, there is still insufficient recognition and accessible treatments. Many mobile apps are available to the general population that aim to support mental health needs; however, there is limited evidence of their effectiveness. Mobile apps for mental health are beginning to incorporate artificial intelligence and there is a need for an overview of the state of the literature on these apps. The purpose of this scoping review is to provide an overview of the current research landscape and knowledge gaps regarding the use of artificial intelligence in mobile health apps for mental health. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Population, Intervention, Comparator, Outcome, and Study types (PICOS) frameworks were used to structure the review and the search.
Introduction
Mental health conditions, such as anxiety and depression, can have significant negative impacts on a range of mental and physical wellbeing, social, and employment outcomes [1,2]. People with severe, long-term mental illness have an average of 15 years shorter life expectancies than the general population [3]. Worldwide, there is a high prevalence of mental health issues and conditions [4]; in the UK, approximately a quarter of the population is seeking mental health treatment [5]. Despite this, it is estimated that 75% of people who need mental health support do not receive it, resulting in costs to the UK economy of approximately £100 billion annually [3,6]. Globally, this cost exceeds US$1 trillion each year [7]. There is a clear need for improved means of identifying and supporting mental health conditions among the general population.
Results
The search of PubMed retrieved 1,228 articles (see S2 Appendix). After duplicates (n = 206) were removed using EndNote X9, 1,022 articles were screened using EndNote’s keyword search. 14 articles remained after 6 rounds of screening (see S3 Appendix) and 4 articles were determined to be eligible for final inclusion upon title/abstract and full-text review. A second screening was conducted with revised keyword searches and identified 17 articles for full text review (see S3 Appendix); 2 were already included, and 13 were determined to be eligible for inclusion, resulting in a total of 17 articles included in the final review (see Fig 1).
Discussion
Of the large number of mobile health apps for mental health [8], many apps—with high ratings and large numbers of downloads—have begun to incorporate artificial intelligence [16–18]. This review shows the diversity and feasibility of the use of artificial intelligence to support mental health care in a variety of different ways. However, it also demonstrates that, so far, there is limited research that can provide evidence of the effectiveness of these apps. Only three randomised controlled trials of an AI-enabled mental health app were identified in this review [28–30] and all of them were small-scale pilot RCTs, despite the availability of several highly-used AI-enabled mental health apps on the Apple App Store and Google Play (e.g. Woebot, Reflectly, Wysa, Youper). This review identifies the strengths and weaknesses in this field and highlights the need for high-quality, rigorous investigation of the AI-enabled mental health apps that are currently available and being used as well as those in development.
Citation: Milne-Ives M, Selby E, Inkster B, Lam C, Meinert E (2022) Artificial intelligence and machine learning in mobile apps for mental health: A scoping review. PLOS Digit Health 1(8): e0000079. https://doi.org/10.1371/journal.pdig.0000079
Editor: Padmanesan Narasimhan, University of New South Wales, AUSTRALIA
Received: October 7, 2021; Accepted: June 22, 2022; Published: August 15, 2022
Copyright: © 2022 Milne-Ives et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data used in this scoping review was extracted from previously published papers and cited in the text and reference list.
Funding: This research was funded by the NIHR Artificial Intelligence in Health and Care Award (grant reference number: AI_AWARD02176). The views expressed in the paper belong to the authors and not necessarily those of NIHR, the University of Plymouth, or Wysa Ltd. The funding bodies were not involved in the study design, data collection or analysis, or the writing and decision to submit the article for publication.
Competing interests: ES is an employee of, and BI is an advisor for, Wysa Ltd. a company that has designed and developed an AI-enabled mental health app. ES and BI contributed to the initial conception of the study. No employees of Wysa were involved in the manuscript’s final drafting.
