About this Research Topic
Applications of Artificial Intelligence (AI) are currently being explored to support mental health. Researchers are developing a range of AI systems underlying clinician-facing tools for improving the delivery of care, and patient-facing tools capable of disseminating health information, detecting symptoms or risks, and even delivering interventions. Despite the extensive social and material implications of implementing such tools, a large focus of this research has been on improving the performance of AI or refining its technical features. Accordingly, tools are being developed without a meaningful consideration of clinical context and the broader social, economic, and political systems in which they will be embedded. Unfortunately, the consequences of such oversights have emerged in other areas of healthcare. When evaluated in real-world settings. AI tools have been shown to provide harmful recommendations or exacerbate inequities, which presents a major challenge to responsible implementation.
This translational gap emerging in other areas of health can serve as an important lesson; it underscores the value of adopting a sociotechnical lens early in the process of developing AI applications for mental health. In this context, 'sociotechnical' refers to the interrelation of social and technical factors that are likely to impact implementation. These various factors are deeply intertwined, and they can often have conflicting effects, which are challenging to measure and anticipate. However, it is necessary to understand how the technical features of AI systems will interact with the social, clinical, economic, and political context to impact mental health and its care. Accordingly, the goal of this Research Topic is to deepen our understanding of the sociotechnical factors impacting AI implementation across a broad spectrum of applications in mental health. It aims to explore the benefits, as well as challenges and harms, emerging from AI integration at any stage, from design and development of AI systems to their evaluation and deployment in clinical and community settings.
This Research Topic aims to publish any findings that can provide much-needed evidence on the sociotechnical requirements for the successful and responsible implementation of AI into mental health care. We invite contributions from diverse fields, including computer science, engineering, health, administration, social science, and related interdisciplinary collaborations.
Examples of relevant research include, but are not limited to:
- theoretical studies on the hypothesized role of sociotechnical factors
- systematic reviews or meta-analyses of sociotechnical factors emerging in prior work
- novel frameworks and methodologies for investigating sociotechnical factors for AI implementation (e.g., interdisciplinary collaborations, use of simulation)
- validation studies or 'silent trials' of AI-based tools, including assessments of impacts on health equity (e.g., fairness audits) or limitations in clinical settings
- studies of AI impacts on the process and delivery of psychiatric care, such as those involving patients, clinicians, administrators, health systems, or the public
This translational gap emerging in other areas of health can serve as an important lesson; it underscores the value of adopting a sociotechnical lens early in the process of developing AI applications for mental health. In this context, 'sociotechnical' refers to the interrelation of social and technical factors that are likely to impact implementation. These various factors are deeply intertwined, and they can often have conflicting effects, which are challenging to measure and anticipate. However, it is necessary to understand how the technical features of AI systems will interact with the social, clinical, economic, and political context to impact mental health and its care. Accordingly, the goal of this Research Topic is to deepen our understanding of the sociotechnical factors impacting AI implementation across a broad spectrum of applications in mental health. It aims to explore the benefits, as well as challenges and harms, emerging from AI integration at any stage, from design and development of AI systems to their evaluation and deployment in clinical and community settings.
This Research Topic aims to publish any findings that can provide much-needed evidence on the sociotechnical requirements for the successful and responsible implementation of AI into mental health care. We invite contributions from diverse fields, including computer science, engineering, health, administration, social science, and related interdisciplinary collaborations.
Examples of relevant research include, but are not limited to:
- theoretical studies on the hypothesized role of sociotechnical factors
- systematic reviews or meta-analyses of sociotechnical factors emerging in prior work
- novel frameworks and methodologies for investigating sociotechnical factors for AI implementation (e.g., interdisciplinary collaborations, use of simulation)
- validation studies or 'silent trials' of AI-based tools, including assessments of impacts on health equity (e.g., fairness audits) or limitations in clinical settings
- studies of AI impacts on the process and delivery of psychiatric care, such as those involving patients, clinicians, administrators, health systems, or the public
Keywords: Contextual factors, Fairness, Health equity., AI, Clinical decision support, Mental Health, Psychiatry, Sociotechnical factors
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
