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The Impact of AI in the Mental Health Field

by | Nov 21, 2025 | Articles and Blogs

The Impact of AI in the Mental Health Field

with Insights from Dr. Alison McInnes and Dr. Tobias Marton | Mindful Health Solutions

 

Love it, hate it, or not so sure about it – AI is reshaping our lives, from the way we create content to how we communicate with one another, and even to how we receive mental health care. AI is rapidly transforming the field of psychiatry by enhancing diagnoses, monitoring, decision support, and access to care, but like all technology, it comes with benefits and risks, requiring responsible integration that addresses ethical, legal, and practical challenges.[1][7][2][4][9] Featuring clinical data and insights from Dr. Tobias Marton, Chief Medical Officer, and Dr. Alison McInnes, Director of Psychedelic Medicine, at Mindful Health Solutions, this blog will take a look at the impact of AI in the mental health field, including its value, downsides, and ethical considerations. 

How Is AI Used in the Mental Health Field?

As its capabilities grow, AI is increasingly being used in mental health practices for exploring patient symptoms, making diagnoses, recommending intervention, and even improving decision-making support. 

Diagnoses
AI algorithms (or, sets of rules a platform uses to sort and display based on user activity) are powerful, complex tools with natural language processing and deep neural networks. Meaning, AI is capable of analyzing health records, physical and mental health symptoms, and even speech and text patterns on a larger scale compared to traditional methods. This helps mental health professionals detect and improve diagnostic accuracy for conditions like depression, anxiety, and schizophrenia, as well as formulate appropriate treatment plans.[1][2][3][4]

Monitoring
When continuous monitoring is needed, AI can be a valuable tool for both clinicians and individuals struggling with their mental health. AI uses data provided by smartphones, social media, and even wearable devices to monitor symptoms, allow for early intervention[5][6], and offer an alternative outlet for patients interested in taking more control over their health. These outlets include AI-driven conversational agents, or chatbots, that can produce a wealth of information on symptoms and treatment options, and virtually deliver therapies like Cognitive Behavioral Therapy (CBT)[7][8][9].

Decision Making
AI can be a valuable tool for medical providers, not only by assisting in diagnosis but also in making treatment and ongoing care recommendations. Clinicians can use AI as a support system in creating prior authorizations, insurance appeals, treatment instructions, and more, tailoring results to individual patients [9][10][11].

Here, it’s important to note that human oversight alongside the use of appropriate AI tools are vital to ensure patient privacy and avoid bias. For example, OpenEvidence, a tool available only to clinicians, offers a “consult” function to help create differential diagnoses for complex mental health cases. OpenEvidence draws data from 13 journals in the prestigious JAMA network to ensure high-integrity research summaries on a wide range of topics that can be used to support prior authorizations and other insurance appeals. On the other hand, AI chatbots and search engines like ChatGPT are trained indiscriminately on any information available via the internet, and thus are not reliable tools for these purposes. 

What Are the Benefits of Using AI in These Ways?

AI tools show promise in improving many areas of the health care industry. Some of the current benefits being analyzed include:

Increased Accessibility
AI chatbots and tools like ChatGPT or Perplexity allow for immediate support whenever and wherever it’s needed. This 24/7 availability breaks down common barriers to care, such as time of day, distance to a medical provider or facility, and lack of understanding about a condition or process. AI is especially helpful for those in underserved areas or who may be hesitant to reach out for care due to cost.

Greater Accuracy
Current studies of various platforms show that AI can be highly accurate in diagnosing a wide range of mental health disorders, such as depression, schizophrenia, and cognitive impairment, by combining multiple data streams and monitoring patient progress. In fact, Large Language Models (LLMs) like Gemini and Claude can offer better performance than traditional depression detection tools, but that performance can be inconsistent across languages and populations.[12][13]

More Clinical Support
AI tools can not only support decision making by offering real-time insights and recommendations, but can also reduce clinical workloads by managing administrative tasks. This includes taking and organizing notes during appointments, allowing doctors to better focus on their patients rather than the computer. Here, it’s important to note that AI tools are still adapting, and their integration into routine medical care must address potential issues related to trust, transparency, cultural adaptability, and ethical concerns (e.g., privacy, bias).[18][19][17] 

Personalized Care
By analyzing patient symptoms, controlled clinical trials, and other data sources, AI can help tailor mental health treatment plans while significantly reducing symptoms of anxiety and depression in the short term, improve engagement, and increase therapy attendance.[14][15][16] In fact, by personalizing treatments with patient-specific recommendations like guided relaxation or mindfulness exercises, studies have shown that AI-supported mental health care can lead to greater depression and anxiety relief and higher session attendance, without compromising patient satisfaction.[15]  

 

In short, AI tools are highly beneficial in enhancing diagnostic accuracy and short-term symptom relief. However, given its relative newness being utilized in such ways, it’s important to remember that more real-world, long-term studies are important to fully analyze their effectiveness and impact on patient care compared to standard practice.[20][18][14][12][19][13][6][15][16]

What Are Potential Downsides of AI in Mental Health Care?

Despite its prevalence and hype, AI is still new in the realm of mental health care. Therefore, most real-world studies are in their early stages, with limited high-quality data on patient results, while the quality of that limited data compared to traditional care standards is still low to moderate.[19][17] [16]  AI also poses concerns with longer-term follow-up and the need for human monitoring, as noted below in ongoing concerns over AI in health care.

Potential for Bias
AI is always learning and evolving, but depending on where and how it learns, results may be skewed if bias is introduced. To date, AI models may reflect existing health care disparities, particularly those relating to minority and low-income groups, if they are trained on non-representative data, which can result in biased diagnoses and treatment plans.

Ethical Concerns
Understandably, key concerns with AI center around patient privacy and data security. While clinicians may use AI tools for diagnoses and treatment recommendations, HIPAA compliance must be maintained at all times to ensure secure and ethical use of patient data. Due to a current lack of oversight, especially around information given to vulnerable populations, many clinicians worry that using AI in their practice may introduce more challenges than opportunities.

Lack of Social & Clinical Engagement
As with any virtual tool, reliance on AI, particularly conversational chatbots, can lead to social withdrawal, and this lack of human connection may increase symptoms of depression due to isolation and loneliness. Studies have also shown that over-reliance on AI results in a lack of in-person care. For example, one analysis revealed substantial improvements in depression and anxiety after 8 weeks of chatbot use, but no significant effects at 3 months, suggesting that these short-term benefits may not be sustained without ongoing engagement.[23][24][25][26]

No Human Connection
Just as a lack of human connection can increase loneliness, so too can it affect proper care. Critics of AI argue that AI lacks empathy and genuine understanding of mental health disorders, as well as the more nuanced understanding of a human therapist – all of which are core components of successful treatment. This lack may even lead to safety concerns given the inherent risk for harmful AI responses, such as AI chatbots suggesting dangerous or inappropriate responses to individuals expressing suicidal thoughts or ideation. 

 

In summary, whether using AI for personal or clinical purposes, it’s important to keep these potential downsides in mind to protect your privacy or that of a patient. These are also good reminders that while AI can be a beneficial mental health tool, they are not a replacement for genuine human interaction. 

 

How Does Mindful Health Solutions Use AI to Further Its Mission in Providing Exceptional Interventional Psychiatry?

Mindful Health Solutions uses AI in moderation, and in specific instances to aid in administrative tasks. This includes an AI scribe called Nudge, which allows clinicians to securely record sessions and provide insights into the effectiveness of their own psychotherapeutic approaches. “Self-reflection in medicine is extremely important,” concludes Dr. McInnes, “and Nudge provides an opportunity to change the course of treatment if indicated” (an activity that’s also a core component of board re-certification). Nudge also speeds up notetaking, allowing clinicians to focus directly on the patient (rather than splitting the time with the keyboard) and can offer billing and coding strategies.

AI is also used to generate treatment approaches according to each patient’s unique needs, symptoms, and even triggers. For example, using AI to create a careful progression of exposure intensity for individuals with OCD prior to TMS treatment in order to strengthen dysfunctional neural pathways, thereby leading to better results.

 

AI – a New Frontier in Mental Health Care

As outlined here, AI can be a valuable tool in the health care field – but when used responsibly and ethically. This blog touches on the positives and negatives of how both patient and provider alike may incorporate AI into care, and as AI is rapidly evolving, the ways in which we use it will evolve as well. We encourage you to explore AI tools and discover ways it may benefit your life, while reaching out to a mental health professional when you find yourself in need of compassionate, expert care.

 

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References

  1. Artificial Intelligence in Mental Health Care: A Systematic Review of Diagnosis, Monitoring, and Intervention Applications. Cruz-Gonzalez P, He AW, Lam EP, et al. Psychological Medicine. 2025;55:e18. doi:10.1017/S0033291724003295.
  2. Practical AI Application in Psychiatry: Historical Review and Future Directions. Sun J, Lu T, Shao X, et al. Molecular Psychiatry. 2025;:10.1038/s41380-025-03072-3. doi:10.1038/s41380-025-03072-3.
  3. Deep Neural Networks in Psychiatry. Durstewitz D, Koppe G, Meyer-Lindenberg A. Molecular Psychiatry. 2019;24(11):1583-1598. doi:10.1038/s41380-019-0365-9.
  4. Psychiatry in the Age of AI: Transforming Theory, Practice, and Medical Education. Zheng H, Zhang X. Frontiers in Public Health. 2025;13:1660448. doi:10.3389/fpubh.2025.1660448.
  5. Artificial Intelligence: A Game-Changer for Mental Health Care. Dakanalis A, Wiederhold BK, Riva G. Cyberpsychology, Behavior and Social Networking. 2024;27(2):100-104. doi:10.1089/cyber.2023.0723.
  6. Artificial Intelligence for Mental Health and Mental Illnesses: An Overview. Graham S, Depp C, Lee EE, et al. Current Psychiatry Reports. 2019;21(11):116. doi:10.1007/s11920-019-1094-0.
  7. The Application of Artificial Intelligence in the Field of Mental Health: A Systematic Review. Dehbozorgi R, Zangeneh S, Khooshab E, et al. BMC Psychiatry. 2025;25(1):132. doi:10.1186/s12888-025-06483-2.
  8. Artificial Intelligence as a Predictive Tool for Mental Health Status: Insights From a Systematic Review and Meta-Analysis. Humayun A, Madawana AM, Hassan A, et al. PloS One. 2025;20(9):e0332207. doi:10.1371/journal.pone.0332207.
  9. AI in Mental Health: A Review of Technological Advancements and Ethical Issues in Psychiatry. Poudel U, Jakhar S, Mohan P, Nepal A. Issues in Mental Health Nursing. 2025;46(7):693-701. doi:10.1080/01612840.2025.2502943.
  10. The Use of AI in Mental Health Services to Support Decision-Making: Scoping Review. Auf H, Svedberg P, Nygren J, Nair M, Lundgren LE. Journal of Medical Internet Research. 2025;27:e63548. doi:10.2196/63548.
  11. On the Role of Artificial Intelligence in Psychiatry. Rocheteau E. The British Journal of Psychiatry : The Journal of Mental Science. 2023;222(2):54-57. doi:10.1192/bjp.2022.132.
  12. Artificial Intelligence in Mental Health Care: A Systematic Review of Diagnosis, Monitoring, and Intervention Applications. Cruz-Gonzalez P, He AW, Lam EP, et al. Psychological Medicine. 2025;55:e18. doi:10.1017/S0033291724003295.
  13. Practical AI Application in Psychiatry: Historical Review and Future Directions. Sun J, Lu T, Shao X, et al. Molecular Psychiatry. 2025;:10.1038/s41380-025-03072-3. doi:10.1038/s41380-025-03072-3.
  14. Deep Neural Networks in Psychiatry. Durstewitz D, Koppe G, Meyer-Lindenberg A. Molecular Psychiatry. 2019;24(11):1583-1598. doi:10.1038/s41380-019-0365-9.
  15. Psychiatry in the Age of AI: Transforming Theory, Practice, and Medical Education. Zheng H, Zhang X. Frontiers in Public Health. 2025;13:1660448. doi:10.3389/fpubh.2025.1660448.
  16. Artificial Intelligence: A Game-Changer for Mental Health Care. Dakanalis A, Wiederhold BK, Riva G. Cyberpsychology, Behavior and Social Networking. 2024;27(2):100-104. doi:10.1089/cyber.2023.0723.
  17. Artificial Intelligence for Mental Health and Mental Illnesses: An Overview. Graham S, Depp C, Lee EE, et al. Current Psychiatry Reports. 2019;21(11):116. doi:10.1007/s11920-019-1094-0.
  18. The Application of Artificial Intelligence in the Field of Mental Health: A Systematic Review. Dehbozorgi R, Zangeneh S, Khooshab E, et al. BMC Psychiatry. 2025;25(1):132. doi:10.1186/s12888-025-06483-2.
  19. Artificial Intelligence as a Predictive Tool for Mental Health Status: Insights From a Systematic Review and Meta-Analysis. Humayun A, Madawana AM, Hassan A, et al. PloS One. 2025;20(9):e0332207. doi:10.1371/journal.pone.0332207.
  20. AI in Mental Health: A Review of Technological Advancements and Ethical Issues in Psychiatry. Poudel U, Jakhar S, Mohan P, Nepal A. Issues in Mental Health Nursing. 2025;46(7):693-701. doi:10.1080/01612840.2025.2502943.
  21. The Use of AI in Mental Health Services to Support Decision-Making: Scoping Review. Auf H, Svedberg P, Nygren J, Nair M, Lundgren LE. Journal of Medical Internet Research. 2025;27:e63548. doi:10.2196/63548.
  22. On the Role of Artificial Intelligence in Psychiatry. Rocheteau E. The British Journal of Psychiatry : The Journal of Mental Science. 2023;222(2):54-57. doi:10.1192/bjp.2022.132.
  23. Artificial Intelligence as a Predictive Tool for Mental Health Status: Insights From a Systematic Review and Meta-Analysis. Humayun A, Madawana AM, Hassan A, et al. PloS One. 2025;20(9):e0332207. doi:10.1371/journal.pone.0332207.
  24. Systematic Review and Meta-Analysis of AI-based Conversational Agents for Promoting Mental Health and Well-Being. Li H, Zhang R, Lee YC, Kraut RE, Mohr DC. NPJ Digital Medicine. 2023;6(1):236. doi:10.1038/s41746-023-00979-5.
  25. Effectiveness of AI-Driven Conversational Agents in Improving Mental Health Among Young People: Systematic Review and Meta-Analysis. Feng Y, Hang Y, Wu W, et al. Journal of Medical Internet Research. 2025;27:e69639. doi:10.2196/69639.
  26. The Therapeutic Effectiveness of Artificial Intelligence-Based Chatbots in Alleviation of Depressive and Anxiety Symptoms in Short-Course Treatments: A Systematic Review and Meta-Analysis. Zhong W, Luo J, Zhang H. Journal of Affective Disorders. 2024;356:459-469. doi:10.1016/j.jad.2024.04.057.

 

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