Artificial intelligence: An eye cast towards the mental health nursing horizon

Wilson, Rhonda L.; Higgins, Oliver; Atem, Jacob; Donaldson, Andrea E.; Gildberg, Frederik Alkier; Hooper, Mary; Hopwood, Mark; Rosado, Silvia; Solomon, Bernadette; Ward, Katrina; Welsh, Brandi

doi:http://dx.doi.org/10.1111/inm.13121

Artificial intelligence: An eye cast towards the mental health nursing horizon

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Wilson RL, Higgins O, Atem J, Donaldson AE, Gildberg FA, Hooper M, et al. Artificial intelligence: An eye cast towards the mental health nursing horizon. Int J Ment Health Nurs. 2023 Jun;32(3):938-44. DOI: 10.1111/inm.13121

Abstract

There has been an international surge towards online, digital, and telehealth mental health services, further amplified during COVID-19. Implementation and integration of technological innovations, including artificial intelligence (AI), have increased with the intention to improve clinical, governance, and administrative decision-making. Mental health nurses (MHN) should consider the ramifications of these changes and reflect on their engagement with AI. It is time for mental health nurses to demonstrate leadership in the AI mental health discourse and to meaningfully advocate that safety and inclusion of end users' of mental health service interests are prioritized. To date, very little literature exists about this topic, revealing limited engagement by MHNs overall. The aim of this article is to provide an overview of AI in the mental health context and to stimulate discussion about the rapidity and trustworthiness of AI related to the MHN profession. Despite the pace of progress, and personal life experiences with AI, a lack of MHN leadership about AI exists. MHNs have a professional obligation to advocate for access and equity in health service distribution and provision, and this applies to digital and physical domains. Trustworthiness of AI supports access and equity, and for this reason, it is of concern to MHNs. MHN advocacy and leadership are required to ensure that misogynist, racist, discriminatory biases are not favoured in the development of decisional support systems and training sets that strengthens AI algorithms. The absence of MHNs in designing technological innovation is a risk related to the adequacy of the generation of services that are beneficial for vulnerable people such as tailored, precise, and streamlined mental healthcare provision. AI developers are interested to focus on person-like solutions; however, collaborations with MHNs are required to ensure a person-centred approach for future mental healthcare is not overlooked.