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Artificial Intelligence in Psychological Intervention: An Initial Reflection

In recent years, the integration of Artificial Intelligence (AI) and Large Language Models (LLMs) in psychological intervention has sparked growing interest within the scientific community and among mental health professionals. This phenomenon is not surprising, given the promise these technologies hold in enhancing the reach, personalization, and effectiveness of psychological interventions.

Recent research has demonstrated that the use of AI and LLMs can be effective in various areas of psychological intervention. For example, AI-based chatbots and virtual assistants have been successfully used to provide emotional support and short-term therapeutic interventions. These tools, validated in studies, can indeed help reduce symptoms of anxiety and depression, especially in populations that may not have easy access to mental health services.

This technology has also been used in applications that analyze large volumes of data to identify patterns and predict which therapeutic approaches might be most effective for each individual. This personalization can significantly improve treatment outcomes, allowing for more precise interventions tailored to the specific needs of each patient.

One of the main benefits of integrating AI into psychological intervention is the improvement of accessibility. AI-based tools can provide continuous and immediate support, which is especially valuable in contexts where resources are limited or where there is a shortage of mental health professionals. Additionally, these technologies can help reduce the stigma associated with seeking help by offering a discreet and confidential alternative.

The capability of this technology to analyze large quantities of data also represents a significant advantage. Professionals who can leverage innovative tools may benefit from identifying trends and patterns that can inform the development of new interventions and improve existing clinical practices.

However, there are several areas where the integration of AI and LLMs in psychological intervention can be improved. One of the main concerns is the issue of data privacy and security. It is crucial to ensure that data is protected and that AI tools adhere to strict ethical and privacy standards. It is also mandatory for professional teams developing these tools to be aware of their legal obligations. For instance, European legislation (i.e., AI Act) already considers a series of compliance requirements that organizations must adhere to.

Another area for improvement is the accuracy and sensitivity of AI-based interventions. While chatbots and other AI tools can be effective in providing basic support, there are still limitations in their ability to handle complex or emergency cases. Human intervention remains essential in many scenarios, and AI should be seen as a complementary tool, not a substitute.

The integration of AI and LLMs in psychological intervention presents significant potential to transform the field of mental health. The benefits in terms of accessibility, personalization, and effectiveness are promising, but it is necessary to carefully address issues of privacy, security, and the need for continuous human intervention. As research progresses, it is expected that these technologies will become even more integrated into clinical practices, improving care and outcomes for those benefiting from mental health services worldwide.

Director at RUMO | Clinical psychologist and Specialist in clinical and health psychology


Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19.

Gaffney, H., Mansell, W., & Tai, S. (2019). Compassionate Mind Training for University Students: A Randomized Controlled Trial of a Self-Help Programme to Enhance Compassion. Self and Identity, 18(2), 145-163.

Inkster, B., Sarda, S., & Subramanian, V. (2018). An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study. JMIR Mhealth Uhealth, 6(11), e12106.

Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18, 43-49.

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., & Bartels, S. J. (2016). The future of mental health care: peer-to-peer support and social media. Epidemiology and Psychiatric Sciences, 25(2), 113-122.


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