Background
As demand for better mental health services continues to grow worldwide, generative artificial intelligence (AI) has the potential to offer a new and scalable solution to fill existing gaps in care delivery. Therapists have begun employing generative AI in therapy through an expanding range of applications, including chatbot technology.
Access to mental health support continues to increase as companies continue to enhance the capabilities of their generative AI technologies and as more individuals invest in digital health tools that provide them with access to cognitive behavioral therapy (CBT).
However, even though many AI mental health chatbots hold great promise, there are still many questions about their efficacy and safety (American Psychological Association, 2023).
The Rise of Generative AI in Mental Health Care
Generative AI’s growth in psychology shows how technology is evolving and how mental health professionals have unmet clinical needs, driving increased interest in generative AI development for healthcare applications.
As opposed to traditional or rule-based machines, generative AI responds to the context of the conversation itself. This has led to a more human-like interaction. There has been an increase in the use of generative AI tools to help manage anxiety, depression, and stress; this is evidenced in a recent systematic review, which has revealed how rapidly generative AI has been adopted and the scope of its growing use among different population groups.
Potential of Generative AI in Therapy
Knowing how AI therapy can help explains why healthcare systems are putting money into these tools.
1. Increased Availability of Care
The primary advantage of using AI mental health technology is that it increases the ability of people to get mental health care when they need it most.
AI technologies can provide users with quick and inexpensive support, especially in remote settings or crises. This will be especially helpful for people who cannot access or wait for traditional therapy services.
2. Clinical Effectiveness Evolving
To understand AI better and how to utilize the digital therapy tool, we have to look at how well it helps as a form of treatment and measure that by using evidence-based methods for measuring treatment success. Previous research has shown that digital therapy tools can be effective in helping individuals decrease their level of anxiety and/or depression, especially if they are experiencing mild/moderate symptoms (Fitzpatrick et al., 2017).
3. Customized & Ongoing Support
AI allows a level of customization and availability that is frequently unattainable by traditional therapy models.
Continuously and automatically (through generational AI), AI can modify the response to the user’s input and provide ongoing, supportive systems for the user. The non-judgmental perceptions associated with these tools may encourage users to share their experiences and, therefore, increase the likelihood that they will engage in therapeutic practices.
4. Strengthened Hybrid Care Models
AI will increasingly be used in conjunction with clinicians in order to improve the effectiveness and efficiency of service delivery in hybrid care models.
Within hybrid care models, AI can aid clinicians in their psycho-educational delivery, follow-up, and engagement with their patients between sessions. Experts agree that the synergy of AI and human therapists has the potential to increase both the quality and quantity of care (Hipgrave et al., 2025).
Challenges and Risks Associated with AI in Psychotherapy
Although generative artificial intelligence has its benefits, it presents both important clinical and ethical issues.
1. Safety & ethics concerns
Safety and ethics are very important issues related to AI mental health-related tools. The safety (i.e., reliability and safety of AI-based mental health tools) cannot be compromised in the interest of using AI tools for mental health purposes. Most of the mental health systems that use AI have not yet been clinically validated and do not yet have regulatory oversight, making a safety-based experience uncertain. As a result, the algorithm(s) within AI systems may lead to poor or unreliable recommendations provided by the AI system.
2. Risk of over-reliance
As people become more dependent on AI tools, some people may need to change their approach to seeking out care in the future.
Individuals who seek to have their needs met by AI tools may utilize them instead of professionals and may become emotionally dependent on AI tools (i.e., chatbots). When professionals are needed for treatment, there may be delays in obtaining a needed, qualified professional.
3. Not enough emotional depth
While AI is designed to provide helpful responses, it has no emotion and does not bond with an individual. To date, there is some evidence that human therapists provide better patient outcomes and would be a better fit for patients who are experiencing more severe, complicated problems than would an AI-based professional.
Ethics & Regulations
As Artificial Intelligence gets increasingly used in the Healthcare System, so too must governance frameworks evolve appropriately.
Accountability, Transparency, and Privacy of Data are all very important issues when integrating AI into healthcare systems. Professional experts agree that clinical standards should be well defined, and regulatory oversight should exist to allow for safe integration of AI technology (Hipgrave et al., 2025).
Future Role of AI for Therapeutic Purposes
The use of artificial intelligence in providing mental health care will depend heavily on the cooperation between human professional skills and technical capabilities.
Generative AI will be able to help increase the scale of mental health care and allow clinicians to spend their time treating the more complex cases. The greatest success may be achieved by establishing research-based hybrid systems that utilize both AI-based and traditional human-based mental health practitioner models.
Conclusion
To sum things up, generative AI in therapy has the potential to make substantial contributions to how mental health is delivered via a variety of means (access and personalization), but it also has limitations (e.g., underdeveloped capacity to provide empathy, safety, and equity).
The current data suggests generative AI should be considered to be a co-delivery (supplemental) means where services could be delivered as part of a much broader continuum of care.
References
- American Psychological Association. (2023). Health advisory on AI chatbots and mental health. https://www.apa.org/topics/artificial-intelligence-machine-learning/health-advisory-chatbots-wellness-apps
- Hipgrave, L., Goldie, J., Dennis, S., & Coleman, A. (2025). Balancing risks and benefits: clinicians’ perspectives on the use of generative AI chatbots in mental healthcare. Frontiers in Digital Health, 7, 1606291. https://doi.org/10.3389/fdgth.2025.1606291
- Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy using a fully automated conversational agent (Woebot). JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785
Author Bio
Yuliya Melnik is a technical writer at Cleveroad, a software development company that offers generative AI development services. She is passionate about innovative technologies that make the world a better place and loves creating content that evokes vivid emotions.