Ensuring patient and public involvement in the transition to AI‐assisted mental health care: A systematic scoping review and agenda for design justice

Zidaru, Teodor; Morrow, Elizabeth; Stockley, Rich

doi:10.1111/hex.13299

Cited by 37 publications

(41 citation statements)

References 247 publications

(735 reference statements)

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“…However, research to better understand the risks and benefits of using VHAs and other AI-infused technology is required. Public awareness of the potential risks and benefits is necessary for older adult users and other vulnerable populations to make informed choices (35). Continued research, public involvement in product development, and policy to promote ethical, unbiased AI that protects the privacy of users is necessary (35).…”

Section: Discussionmentioning

confidence: 99%

“…Public awareness of the potential risks and benefits is necessary for older adult users and other vulnerable populations to make informed choices (35). Continued research, public involvement in product development, and policy to promote ethical, unbiased AI that protects the privacy of users is necessary (35). In addition, the growth of all types of digital health necessitates devising strategies for affordable and reliable internet access to promote health equity (41,42).…”

Section: Discussionmentioning

confidence: 99%

“…Conversely, AI is also being used to automate discourse analysis which may improve communication between people with dementia and their caregivers in the future (34). Concerns also exist about inequality and cultural and population biases built into technology-driven AI (35). For example, one study noted racial disparities in the automated speech recognition of VHAs (36).…”

Section: Personal and Ethical Considerationsmentioning

confidence: 99%

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Voice-Activated Virtual Home Assistant Use and Social Isolation and Loneliness Among Older Adults: Mini Review

Corbett

Wright

Jones

et al. 2021

Front. Public Health

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A lack of social connectedness is common among older adults due to living alone, loss of loved ones, reduced mobility, and, more recently, social distancing created by the global Covid-19 pandemic. Older adults are vulnerable to social isolation and loneliness, which pose significant health risks comparable to those of smoking, obesity, physical inactivity, and high blood pressure. A lack of social connectedness is also correlated with higher mortality rates even when controlling for other factors such as age and comorbid conditions. The purpose of this mini review was to explore the emerging concepts of older adults' use of commercially available artificial intelligent virtual home assistants (VHAs; e.g., Amazon Echo, Google Nest), and its relationship to social isolation and loneliness. A secondary purpose was to identify potential areas for further research. Results suggest that VHAs are perceived by many older adult users as “companions” and improve social connectedness and reduce loneliness. Available studies are exploratory and descriptive and have limited generalizability due to small sample sizes, however, similar results were reported across several studies conducted in differing countries. Privacy concerns and other ethical issues and costs associated with VHA use were identified as potential risks to older adults' VHA adoption and use. Older adults who were using VHAs expressed the need and desire for more structured training on device use. Future research with stronger methods, including prospective, longitudinal, and randomized study designs are needed. Public education, industry standards, and regulatory oversight is required to mitigate potential risks associated with VHA use.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Personal and Ethical Considerationsmentioning

confidence: 99%

See 1 more Smart Citation

Voice-Activated Virtual Home Assistant Use and Social Isolation and Loneliness Among Older Adults: Mini Review

Corbett

Wright

Jones

et al. 2021

Front. Public Health

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“…Others have focused their studies on specific AI applications for health care, such as predictive medicine, diagnostics, and clinical decision-making [ 9 , 30 , 34 , 35 ]. A few reviews have been more overarching, focusing on coproduction processes [ 36 ], implementation frameworks [ 37 ], and critical implementation barriers or success factors [ 38 ] that could inform the development of relevant implementation strategies of AI technology. Generally, it is argued that the implementation of AI in health care could significantly improve patient and health care outcomes, but none of these reviews have actually explored the knowledge base of real-world implementation in everyday clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Sharma¹,

Savage²,

Nair³

et al. 2022

J Med Internet Res

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Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has 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. Objective The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). 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. Conclusions Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of 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 in 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.

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“…A few examples contain the examination of suicide notes as well as later periods [56]. Studies on an individual basis have attempted to forecast many things, such as patient stress [57], the likelihood of re-hospitalization for outpatients with severe psychological issues [58], and manic or depressive episodes concerning people with bipolar disorder [59].…”

Section: Question 2: What Aspects Of Mental Health Do the Chatbots Ta...mentioning

confidence: 99%

Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

Omarov¹,

Narynov²,

Zhumanov³

2023

Computers, Materials &Amp; Continua

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Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AIenabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from "virtual psychiatrists" to "social robots" in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings. Furthermore, treatments are frequently developed without clear ethical concerns. While AI-enabled solutions show promise in the realm of mental health, further research is needed to address the ethical and social aspects of these technologies, as well as to establish efficient research and medical practices in this innovative sector. Moreover, the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbotpsychologists development. Taking into account all the problems outlined in this study, we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence. In this systematic review, we put five research questions related to technologies in chatbot development, psychological disorders that can be treated by using chatbots, types of therapies that are enabled in chatbots, machine learning models and techniques in chatbot psychologists, as well as ethical challenges.

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Ensuring patient and public involvement in the transition to AI‐assisted mental health care: A systematic scoping review and agenda for design justice

Cited by 37 publications

References 247 publications

Voice-Activated Virtual Home Assistant Use and Social Isolation and Loneliness Among Older Adults: Mini Review

Voice-Activated Virtual Home Assistant Use and Social Isolation and Loneliness Among Older Adults: Mini Review

Artificial Intelligence Applications in Health Care Practice: Scoping Review

Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

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