Digital Mental Health Research Priorities, Revisited for the AI and Large Language Model Era

Clinical Snapshot

Key Findings

• Effect Size: Not applicable — no quantitative effect sizes reported

• Primary Outcome: Articulation of updated research priorities for digital mental health in the LLM era, reaffirming and extending the 2023 JMIR Mental Health framework across five domains: equity, replicability, privacy, efficacy, and engagement

• Nnt Or Sensitivity: Not applicable — editorial opinion; no diagnostic, therapeutic, or prognostic statistics reported

• Confidence Interval: Not applicable — no statistical estimates provided

Clinical Application

The research priorities articulated are feasible to pursue within existing academic and health system infrastructure, though they require significant investment in interdisciplinary collaboration, regulatory engagement, and longitudinal study design. The call for transparency about technologies studied is immediately actionable for researchers and journal editors. In Australia, the TGA regulates Software as a Medical Device (SaMD) under the Therapeutic Goods (Medical Devices) Regulations 2002, and LLM-based mental health tools meeting the definition of SaMD require conformity assessment. The Australian Digital Health Agency's National Digital Health Strategy 2023–2028 and the RACGP's digital health position statements provide relevant governance frameworks. The priorities of equity and privacy align with Australia's obligations under the Privacy Act 1988 and the My Health Records Act 2012. PBS listing of digital therapeutics remains nascent, and the research gaps identified in this editorial — particularly around efficacy and safety — are directly relevant to future TGA and MSAC submissions. The emphasis on equity is particularly salient given persistent mental health access disparities in rural, remote, and First Nations communities in Australia. Mental health clinicians, digital health researchers, health service administrators, and policymakers considering the adoption or evaluation of LLM-enabled or AI-assisted digital mental health tools in clinical practice

Abstract

Digital mental health has become an established part of mental health care, but the rapid arrival of large language models and other artificial intelligence (AI) tools has refocused attention on the evidence needed to guide the field. This editorial updates the research priorities articulated by JMIR Mental Health in 2023, while reaffirming their emphasis on equity, replicability, privacy, efficacy, and engagement. While the importance of these priorities has not changed in recent years, the urgency with which they must now be applied has. As digital tools become more clinically consequential, research must move beyond demonstrating that a technology is feasible, usable, or novel. The field now needs studies that clarify how these tools work, for whom they are beneficial, under what conditions they may cause harm, and how they can be ethically integrated into care. We call for research that is transparent about the technologies being studied, grounded in meaningful clinical questions, attentive to safety, and designed to produce knowledge that remains useful as specific products and models change.

References

1. 1.Birk, M. V., Kochhar, S., Myrick, K., Schueller, S. M., & Torous, J. (2026). Digital mental health research priorities, revisited for the AI and large language model era. JMIR Mental Health. https://doi.org/10.2196/104118