JMIR Publications

ABSTRACT

Background:

Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, especially in the support of decision-making for healthcare professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to the support of decision-making from technology, end-users, and organizational perspectives with the AI disruption of care processes.

Objective:

This study aims to explore the use of AI systems in mental health to support decision-making, focusing on three key areas: (1) the characteristics of research on AI systems in mental health; (2) the current applications, decisions, end-users, and user flow of AI systems to support decision-making; (3) the evaluation of AI systems for the implementation of support for decision-making including elements influencing the long-term use.

Methods:

A scoping review of empirical evidence was conducted across five databases: PubMed, Scopus, PsycINFO, Web of Science, and CINAHL. The searches were restricted to peer-reviewed articles published in English after 2011. The initial screening on the title and abstract level was conducted by two reviewers, followed by full-text screening based on the inclusion criteria. Data were then charted and prepared for data analysis.

Results:

Twelve of a total of 1217 articles met the inclusion criteria. These studies predominantly originated from high-income countries. The AI systems were used in healthcare, self-care, and hybrid care contexts, addressing a variety of mental health problems. Three types of AI systems were identified in terms of decision-making support: diagnostic and predictive AI, treatment selection AI, and conversational AI. The dynamics of the type of end-user interaction and system design were diverse in complexity for the integration and use of the AI systems to support decision-making in care processes. The evaluation of the use of AI systems highlighted several challenges impacting the implementation and functionality of the AI systems in care processes, including factors affecting accuracy, increase of demand, trustworthiness, patient-physician communication, and engagement with the AI systems.

Conclusions:

The design, development, and implementation of AI systems to support decision-making present significant challenges for the sustainable use of this technology in care processes. The empirical evidence shows that the evaluation of the use of AI systems in mental health is still in its early stages, with need for more empirically focused research of real-world use. The key aspects requiring further investigation include the evaluation of the use of AI supported decision-making utility from human-AI interaction and HCI perspectives, longitudinal implementation studies of AI systems in mental health to assess the use, and the integration of shared decision-making (SDM) in AI systems.