Clinical applications of AI in mental health care
- Submission status
- Open
- Submission deadline
In 1966, the first AI-powered mental health chatbot, Eliza was introduced. Now 57 years later, interest in AI has resurged in light of the chronic mental health workforce shortage and rising need for services. Researchers seeking to elucidate the mechanism of actions and preventive efforts are also turning to AI to synthesize genetic, neuroimaging, behavioral, and clinical data to glean new insights into these complex illnesses. While the first-generation AI applications for mental health developed in the 1960s and 1970s did not transform care, recent advances in computing, smartphones, algorithms, large language models, and data provide new opportunities for AI in mental health today. This collection will explore the latest advances and research in the newest generations of AI in mental health with a focus on clinically applicable, ethical, and transparent AI research.
npj Mental Health Research and npj Digital Medicine are collaborating on this joint collection to curate novel research that looks beyond feasibility to highlight how AI can be safely, ethically, and impactfully utilized to advance the understanding of mental illnesses and deliver better care to patients with these conditions. Reviews synthesizing current knowledge to help guide future developments are also encouraged as well as Perspectives on or from patient experiences, underserved communities, and technology developers. All submissions should be clinically focused in theme and feature methods that are transparent/reproducible in line with best practices and Nature Portfolio submission guidelines and editorial policies.
Each journal will consider submissions covering:
| npj Mental Health Research | npj Digital Medicine |
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The effectiveness of AI-assisted interventions or comparing AI-based approaches with traditional therapeutic methods in real-world clinical settings and clinical trials
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Readily reproducible and validated AI-based diagnostic tools and predictive models for mental health problems and mental health outcomes, such as treatment response, relapse rates, or suicide risk |
| The ethical challenges related to the use of AI in mental health, such as privacy concerns, informed consent, and potential biases in AI algorithms | AI-powered digital interventions such as chatbots, virtual therapists, or mobile apps designed to provide mental health support, deliver therapy, or promote self-management |
| The real-world implementation and integration of AI technologies into existing mental health systems, including considerations of feasibility, acceptance, and barriers | The analysis of large-scale mental health data sets using AI techniques to identify patterns, risk factors, or treatment efficacy |
| Human experiences of interacting with AI systems in mental health contexts, including patient outcomes, acceptance, trust, clinician-patient relationships, and perceptions of AI's role in the therapeutic process. | The application of AI technologies to develop personalized treatment plans or recommendations based on individual characteristics, preferences, and treatment history |
This Collection supports and amplifies research related to SDG 3.
Editors
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Eric Kuhn, PhD
A National Center for PTSD Dissemination and Training Division and Stanford University, USA
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Maurice D Mulvenna, PhD
Ulster University, UK
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John B. Torous, MD MBI
Beth Israel Deaconess Medical Center, Harvard Medical School, USA
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Raymond Bond, PhD
Ulster University, UK
All articles have undergone npj Mental Health Research’s and npj Digital Medicine's standard peer review process and have been subject to all the journal’s standard policies. This includes the journal’s policy on competing interests. The Editors declare no competing interests with the submissions which they have handled through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.
If accepted for publication, an article processing charge (APC) charge applies (with standard waiver policy), please see here for npj Mental Health Research’s APC information and here for npj Digital Medicine’s APC information. If your institution or country has an open access agreement with Springer Nature, you may publish your article OA at no cost or in some cases with the costs partially funded (see details here).
All Collections are open for submissions to all authors – and not by invitation only – on the condition that the manuscripts fall within the scope of the Collection and of npj Mental Health Research or npj Digital Medicine more generally.
Manuscripts submitted to an open Collection may be considered unsuitable for inclusion, particularly if they fall outside the scope of the Collection. In such cases, the editorial office will notify the authors, and their manuscript will be considered a regular npj Mental Health Research or npj Digital Medicine submission.
The in-house Editors of npj Mental Health Research and npj Digital Medicine reserve the right to assume responsibility for the management of a Collection at any stage.
