Australian Journal of Psychology

AI in Mental Health

The integration of Artificial Intelligence (AI) into mental health research and practice holds transformative potential. From enhancing operational, educational, and clinical efficiency, to early detection and diagnosis, to personalized interventions and predictive modelling, AI can enhance our understanding of mental health conditions and optimize care delivery at scale. This Article Collection invites contributions that explore the promises and limitations of AI across diverse domains of mental health, including psychology, public health, neuroscience, and neurocognitive research. Work addressing ethical considerations, algorithmic fairness, cross-cultural validity, education enhancement, implementation, and human-AI collaboration is highly encouraged. By fostering interdisciplinary dialogue, this Collection aims to bridge gaps between data science, psychology, and applied practice—highlighting not only technological innovations, but also the lived experiences and needs of clients and practitioners. We invite researchers, practitioners, ethicists, educators, and technologists to contribute to this fast growing and important area.

Mental health systems worldwide are under increasing pressure from rising demand, limited resources, and uneven access to care. AI-based innovations can address these challenges—enabling earlier detection, enhanced personalized treatment, improved interdisciplinary education, and providing novel care pathways. However, realizing these benefits requires critical examination of how AI is developed, validated, and deployed into real-world practice. As with other healthcare domains, without careful attention to policy, ethics, bias, equity, and implementation, AI could unintentionally widen disparities. In the case of mental health, given social stigma and high sensitivity of data, this becomes even more important. Thus, this Article Collection is important because it advances dialogue on responsible, human-centered AI, contextualized to the specific issues in mental health.

This Article Collection focuses on the emerging role of AI in transforming mental health research, care, and systems. We invite papers that span foundational AI or data science research, applied methodologies (qualitative, quantitative, or mixed), and critical perspectives on how AI can support mental health outcomes at the clinical, community, and population levels. Subtopics include (but are not limited to):

• Early detection and diagnosis using multimodal data
• Explainable AI models
• Predictive modelling for treatment response and relapse
• AI-enabled digital interventions and chatbots
• Ethical, legal, and social implications
• Bias and fairness in model development
• Cultural and contextual relevance of AI applications
• Technologies that augment education
• Implementation science for real-world deployment

We particularly welcome interdisciplinary studies that explore human-AI collaboration in therapeutic settings, participatory design with users and practitioners, and the governance of sensitive mental health data. Innovative education pedagogy specific on the training of mental health stakeholders in enhancing AI and digital literacy are also welcome.

Please contact Dr. MK Huffman at [email protected] with any queries about discount codes regarding this Article Collection.

Dr. Wilson Goh is the Chief Data Scientist at the Centre of AI in Medicine (C-AIM) at Nanyang Technological University and a Senior Lecturer (Associate Professor) at Imperial College London. He also leads the Data Science Research Programme and serves as Co-Director of the Centre for Biomedical Informatics (CBI).

Dr. Goh’s research focuses on leveraging complex multimodal clinical datasets to develop and deploy AI solutions within hospital environments, with a particular emphasis on mental health. His recent work has expanded into implementation science, where he explores data governance issues and investigates how clinicians trust and interact with AI systems. Through his research, Dr. Goh aims to bridge the gap between AI innovation and practical, effective application in healthcare settings.

The Guest Advisors declare no conflict of interest regarding this work.