Artificial intelligence (AI) is poised to transform how mental health conditions are understood, diagnosed and treated, but only if it is implemented carefully, ethically and in partnership with patients and clinicians. A new review paper provides a roadmap to unlocking the potential of AI in mental health research and care, outlining how emerging tools could help close critical gaps in the system while identifying the challenges that must be addressed for real-world impact.
Mental health services across Australia are under increasing strain, with rising demand, workforce shortages and significant variation in how conditions are diagnosed and treated. Unlike many areas of medicine, mental health care relies less on objective biomarkers and more on subjective assessments of behaviour, mood and lived experience by qualified clinicians. According to the review, this is precisely where AI may offer its greatest contribution.
“AI has enormous potential to help us better understand mental health conditions and tailor care to the individual,” says co-author, University of Newcastle Professor Michael Breakspear from HMRI’s Brain Health Research Program.
“But mental health care involves some of the most intimate aspects of personal information, and any technological innovation must earn trust and genuinely support the therapeutic relationship, not replace it.”
The review highlights how machine-learning approaches are already being used in mental health research to analyse complex biological data, including brain imaging, genetics and molecular signalling. By integrating these high-dimensional datasets, researchers hope to improve diagnostic accuracy, predict disease trajectories and identify which treatments are most likely to work for a given individual.
However, the authors warn that many AI models struggle to generalise beyond the specific datasets on which they were trained. Differences between clinics, populations and data-collection methods can limit how well these tools perform when used elsewhere. As part of the roadmap outlined in the review, rigorous validation across multiple sites and real-world settings is essential before AI-driven tools can be safely integrated into routine care.
One of the most promising advances described in the review is the use of smartphones and wearable devices to monitor behaviour and emotional states in everyday life. These technologies enable the collection of data on sleep, movement, social interaction and location, offering unprecedented insight into how mental health fluctuates over time and across environments.
This approach, often referred to as digital phenotyping, allows researchers and clinicians to move beyond snapshot assessments taken during clinic visits. Instead, they can observe patterns and early warning signs as they emerge, forming a key component of the pathway toward earlier intervention and relapse prevention.
The review also examines the growing role of AI-driven digital interventions, including chatbots powered by large language models. Early evidence suggests these tools can support mental health treatment, provide psychoeducation and assist clinicians with decision-making. Their scalability means they could help extend care to people who might otherwise face long wait times or limited access to services.
Yet the authors stress that technology alone is not a solution. As outlined in the roadmap, questions around safety, effectiveness, clinician oversight, data privacy and patient adherence must be carefully addressed. In mental health, where stigma and vulnerability are common, strong ethical guardrails are essential.
Looking ahead, the review emphasises that successfully unlocking the potential of AI in mental health care will require close collaboration between researchers, clinicians, patients and health systems. AI tools could support care at multiple stages, including screening and triage before treatment, real-time feedback during therapy, and long-term monitoring after care has concluded.
Crucially, patients must remain at the centre of these innovations.
“If we design AI around patient needs, support clinicians with proper training, and validate them rigorously in real-world settings, the technology can become a powerful tool in improving mental health outcomes,” says Professor Breakspear.
As mental health challenges continue to grow, the review makes a compelling case that adopting a strategic approach could provide a clear pathway to unlocking the potential of AI in mental health research and care.
