Introduction
Infant mental health (IMH) refers to the developing capacity of the infant and young child (from pregnancy to age three) to experience, express, and regulate emotions; form close and secure relationships; and explore and learn within the caregiving environment, including family, community, and cultural expectations (Australian Association for Infant Mental Health [AAIMH], n.d.). IMH scholarship emphasises that this capacity is inseparable from context: early relationships and the broader ecology of care shape stress regulation, meaning-making, and developmental trajectories (Fitzgerald, 2024; Zeanah & Zeanah, 2019).
Generative artificial intelligence (AI) is now being discussed across mental healthcare for tasks such as drafting and summarising documentation and supporting communication, alongside clear cautions about privacy, bias, and error (Blease & Rodman, 2025; King et al., 2023; Mansoor et al., 2025). The practical question for IMH services is not whether AI can “do” IMH, but whether it can reduce friction around IMH so clinicians and caregivers have more capacity for relational work.
AI as Administrative and Communication Support
IMH work depends on attunement, reflective practice, and relational safety, qualities that require human presence and clinical judgement (Fraiberg, 1980; Zeanah & Zeanah, 2019). AI should not be used to generate diagnoses, conduct risk assessments, replace reflective formulation, or substitute for attachment-informed intervention. Where AI may add value is in supporting the administrative and communication infrastructure around relational care, with accountability remaining with the clinician and service.
One defensible use case is referral and intake clarity. Families often arrive after navigating fragmented pathways and repeating their story. An AI-assisted intake workflow can help structure information into a concise, clinician-facing brief (e.g., presenting concerns, developmental and relational context, caregiver priorities, cultural and language needs, and practical barriers). With appropriate consent and privacy protections, this can reduce duplication and allow early sessions to focus on building safety and understanding the dyad (AAIMH, n.d.; Zeanah & Zeanah, 2019).
A second use case is caregiver-facing psychoeducation written in plain language. IMH requires translating complex developmental and relational concepts into practical, non-stigmatising guidance tailored to each family and culture (Fitzgerald, 2024). AI can assist by drafting plain-language explanations or handouts that the clinician edits, ensuring they are accurate, culturally appropriate, and aligned with the therapeutic stance.
A third use case is documentation efficiency without losing reflective depth. AI can help draft session summaries, letters, and report templates from clinician prompts, provided the clinician verifies the content and signs off. Mental healthcare reviews consistently recommend treating generative AI outputs as drafts, because hallucinations and subtle errors remain possible (Blease & Rodman, 2025; King et al., 2023). A simple operational rule that fits IMH is “Draft–Verify–Sign.”
Guardrails for Relational Safety in IMH
Because IMH involves sensitive family narratives and potential stigma, governance must be explicit. Services should minimise data and avoid placing identifiable family information into consumer AI tools; approved systems, role-based access, and clear retention policies are essential. AI-generated language should also be screened for bias and stigma (Blease & Rodman, 2025). AI may support administrative routing, but it should not determine risk level, diagnostic labels, or treatment planning without clinician oversight.
Families should be informed, in plain language, when AI is used to support administrative tasks or documentation and what safeguards are in place. A staged implementation approach, starting with low-risk uses (template drafting, readability support, administrative summaries), evaluating impact, and only then extending cautiously, aligns with broader calls to integrate AI within human-led care with robust safeguards and evaluation (King et al., 2023; Lachman et al., 2024).
Conclusion
IMH is fundamentally relational: early wellbeing develops through caregiving relationships and the contexts that surround them (AAIMH, n.d.; Fitzgerald, 2024; Zeanah & Zeanah, 2019). AI should not be positioned as a substitute for that work. Used cautiously, however, AI can reduce administrative friction, improve communication clarity, and protect clinician time for the reflective, relationship-based processes that define IMH.
References
Australian Association for Infant Mental Health. (n.d.). What is infant mental health? https://www.aaimh.org.au/about-us/what-is-infant-mental-health/
Blease, C., & Rodman, A. (2025). Generative artificial intelligence in mental healthcare: An ethical evaluation. Current Treatment Options in Psychiatry, 12, Article 5. https://doi.org/10.1007/s40501-024-00340-x
Fitzgerald, H. E. (2024). Overview: Infant mental health theoretical perspectives, research in social-emotional and cognitive development, and the importance of context. In J. D. Osofsky, H. E. Fitzgerald, M. Keren, & K. Puura (Eds.), WAIMH handbook of infant and early childhood mental health (pp. 3–10). Springer. https://doi.org/10.1007/978-3-031-48627-2_1
Fraiberg, S. (1980). Clinical studies in infant mental health: The first year of life. Basic Books.
King, D. R., Nanda, G., Stoddard, J., Dempsey, A., Hergert, S., Shore, J. H., & Torous, J. (2023). An introduction to generative artificial intelligence in mental health care: Considerations and guidance. Current Psychiatry Reports, 25(12), 839–846. https://doi.org/10.1007/s11920-023-01477-x
Lachman, A., Gerber, B., Bornman, J., & Smythe, T. (2024). Opportunities to accelerate progress in infant mental health. The Lancet Child & Adolescent Health, 8(8), 551–552. https://doi.org/10.1016/S2352-4642(24)00131-7
Mansoor, M., Hamide, A., & Tran, T. (2025). Conversational AI in pediatric mental health: A narrative review. Children, 12(3), 359. https://doi.org/10.3390/children12030359
Zeanah, C. H., & Zeanah, P. D. (2019). Infant mental health: The science of early experience. In C. H. Zeanah (Ed.), Handbook of infant mental health (4th ed., pp. 5–24). Guilford Press.
Authors
Amatus, Alexander
Australia
Alexander Amatus is a business development lead at TherapyNearMe.com.au, supporting the delivery of accessible, clinician-led mental health services across Australia. His work focuses on responsible, privacy-first implementation of AI in mental health operations and service pathways.
