For decades, artificial intelligence (AI) has been utilized within the field of mental healthcare. This paper aims to examine AI chatbots, specifically as offered through mobile applications for mental healthcare (MHapps), with attention to the social implications of these technologies. For example, AI chatbots in MHapps are programmed with therapeutic techniques to assist people with anxiety and depression, but the promise of this technology is tempered by concerns about the apps' efficacy, privacy, safety and security.
Utilizing a social informatics perspective, a literature review covering MHapps, with a focus on AI chatbots was conducted from the period of January–April 2019. A borrowed theory approach pairing information science and social work was applied to analyze the literature.
Rising needs for mental healthcare, combined with expanding technological developments, indicate continued growth of MHapps and chatbots. While an AI chatbot may provide a person with a place to access tools and a forum to discuss issues, as well as a way to track moods and increase mental health literacy, AI is not a replacement for a therapist or other mental health clinician. Ultimately, if AI chatbots and other MHapps are to have a positive impact, they must be regulated, and society must avoid techno-fundamentalism in relation to AI for mental health.
This study adds to a small but growing body of information science research into the role of AI in the support of mental health.
The author would like to thank Drs Colin Rhinesmith and Rong Tang for their guidance on this manuscript, and Ceilyn Boyd, Aimee Melvin, Adam McInnish, Bill Ames, Kathy Da Silva, and Corey Rainboth for their support. Funding for presentation of this research at the 2020 International Conference on Internet Technologies and Society was provided by Simmons University.
Gamble, A. (2020), "Artificial intelligence and mobile apps for mental healthcare: a social informatics perspective", Aslib Journal of Information Management, Vol. 72 No. 4, pp. 509-523. https://doi.org/10.1108/AJIM-11-2019-0316
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