Digital Futures in Mind: Reflecting on Technological Experiments in Mental Health &amp;amp; Crisis Support

Bossewitch, Jonah; Brown, Lydia X. Z.; Gooding, Piers; Harris, Leah; Horton, James Africanus Beale; Katterl, Simon; Myrick, Keris Jän; Ubozoh, Kelechi; Encalada, Alberto Vásquez

doi:10.2139/ssrn.4215994

Cited by 6 publications

(17 citation statements)

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“…Consideration must also be given to the clinical and professional accountability of AI/ML based supported decision making. The accountability for AI systems in the mental health context must be appropriately distributed, with suitable solutions in place (Gooding et al 2022). The attribution of accountability and responsibility is vital for not only those affected using the tools but also for public trust in AI solutions.…”

Section: Discussionmentioning

confidence: 99%

“…The attribution of accountability and responsibility is vital for not only those affected using the tools but also for public trust in AI solutions. There must be adequate opportunity to integrate the objectives, outcomes and trade-offs that come with the use of AI, however clinician accountability is yet to be clearly defined (Gooding et al 2022). Both clinicians and patients, that is, end-users, should be involved in the development and design of new health technologies.…”

Section: Discussionmentioning

confidence: 99%

“…Both clinicians and patients, that is, end-users, should be involved in the development and design of new health technologies. The very people who stand to benefit the most from new technologies in mental health care are often missed in the research design and reporting of innovation in the literature (Gooding et al 2022), their omission compromises user-centred design and denies them authentic contribution in partnership with clinicians to promote development and creation of potentially powerful shared decision making platforms. Finally, as more AI/ML based interventions are introduced into healthcare settings, often presented as a panacea for many problems, using an algorithm that performs well will not necessarily equate to better care.…”

Section: Discussionmentioning

confidence: 99%

“…Future innovations should consider potential iatrogenic harms to ensure safety (Farthing et al 2021), especially with vulnerable or already disadvantaged populations (World Health Organization 2021). Of particular note, there is very limited involvement overall of the inclusion of lived experience service users (Gooding et al 2022), that is, the very people who are supposed to gain the greatest benefit from these innovations. It is recommended that future research be framed to understand better how the use of AI/ML in the delivery of care will affect those with a lived experience of mental illness, especially psychosis (Higgins et al 2022).…”

Section: Lived Experience Attitudes and Acceptancementioning

confidence: 99%

“…AI is an emerging technology with considerable push momentum for application across many fields. Despite this, the current market, especially in mental health, is still speculative in its potential (Gooding et al 2022). Overall, much of the literature remains in pilot or exploratory stages and is most frequently located in the computer science field (Gooding & Kariotis 2021).…”

Section: Ethical Implicationsmentioning

confidence: 99%

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Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review

Higgins

Short

Chalup

et al. 2023

Int J Mental Health Nurs

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An integrative review investigating the incorporation of artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health care settings was undertaken of published literature between 2016 and 2021 across six databases. Four studies met the research question and the inclusion criteria. The primary theme identified was trust and confidence. To date, there is limited research regarding the use of AI‐based decision support systems in mental health. Our review found that significant barriers exist regarding its incorporation into practice primarily arising from uncertainty related to clinician's trust and confidence, end‐user acceptance and system transparency. More research is needed to understand the role of AI in assisting treatment and identifying missed care. Researchers and developers must focus on establishing trust and confidence with clinical staff before true clinical impact can be determined. Finally, further research is required to understand the attitudes and beliefs surrounding the use of AI and related impacts for the wellbeing of the end‐users of care. This review highlights the necessity of involving clinicians in all stages of research, development and implementation of artificial intelligence in care delivery. Earning the trust and confidence of clinicians should be foremost in consideration in implementation of any AI‐based decision support system. Clinicians should be motivated to actively embrace the opportunity to contribute to the development and implementation of new health technologies and digital tools that assist all health care professionals to identify missed care, before it occurs as a matter of importance for public safety and ethical implementation. AI‐basesd decision support tools in mental health settings show most promise as trust and confidence of clinicians is achieved.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Lived Experience Attitudes and Acceptancementioning

confidence: 99%

Section: Ethical Implicationsmentioning

confidence: 99%

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