Generative artificial intelligence (gen AI) and Large Language Models (LLMs) for Solo Mental Health Telehealth Practitioners
- David Larsen
- May 24, 2024
- 4 min read
Vol. 1, No. 3 | May 21, 2024 | By Dave Larsen
Generative artificial intelligence (gen AI) and large language models (LLMs) are catalyzing a profound transformation across all personal and professional domains.
Furthermore, these models have demonstrated a remarkable capacity to engage with enterprise data spanning diverse industries, languages, and specializations, effectively embodying omniscient conversational agents. Collectively, gen AI and LLMs are ushering in a new era characterized by unprecedented levels of connectivity and efficiency.
Solo mental health practitioners, such as therapists, counselors, and psychologists, need to be aware of the potential impact that the proliferation of AI language models (LLMs) can have on their practice.
Here are some key considerations:
Ethical and legal implications:
LLMs raise concerns about privacy, confidentiality, and the potential for biased or harmful outputs. Practitioners need to understand the ethical and legal implications of using LLMs in their practice and ensure compliance with relevant regulations and guidelines (Bauer et al., 2022; Vaidyam et al., 2019).
Limitations and risks:
LLMs are not a substitute for human therapists and cannot provide the same level of empathy, nuanced understanding, and personalized care. Practitioners should be aware of the limitations and risks of relying too heavily on LLMs, such as the potential for misdiagnosis or inappropriate treatment recommendations (Palanica et al., 2021; Schwalbe et al., 2020).
Client expectations and acceptance:
Some clients may be interested in using LLMs as part of their therapy or mental health support, while others may have reservations or concerns. Practitioners should be prepared to educate clients on the capabilities and limitations of LLMs and manage expectations accordingly (Gaudiano & Siev, 2023).
Integration into practice:
LLMs could potentially be used as supplementary tools for tasks like note-taking, research, or providing general information. However, practitioners should carefully evaluate the suitability and implications of integrating LLMs into their practice and ensure that they do not compromise the quality of care or therapeutic relationship (Rollins et al., 2022).
Continuing education and professional development:
As LLMs continue to evolve, practitioners should stay informed about the latest developments, attend relevant training or workshops, and engage in ongoing professional development to understand the potential impacts on their field (Karras et al., 2021).
Ethical decision-making:
Practitioners should develop a framework for ethical decision-making when considering the use of LLMs in their practice. This may involve consulting with professional associations, ethics committees, or legal experts to ensure that they are acting in the best interests of their clients (Bauer et al., 2022; Mahoney et al., 2020).
Therapeutic alliance and human connection:
While LLMs may offer some potential benefits, practitioners should prioritize the importance of the human therapeutic alliance and the unique value of in-person, personalized care (Norcross, 2010; Wampold, 2015).
These advanced technologies are enabling "digital assistants", with augmented capabilities to streamline tasks (i.e. email management, customer service response, content creation and analysis, etc.), thereby enhancing convenience and productivity.
Here are five common fears regarding generative AI (gen AI) and large language models (LLMs):
Fear of job displacement and economic disruption:
While AI will certainly automate some tasks, history has shown that technological advancements create new types of jobs and industries. By embracing AI, we can focus on higher-value work and drive economic growth.
Fear of AI systems exhibiting bias or promoting harmful content
It's crucial to prioritize ethical AI development and implement robust safeguards to mitigate bias and harmful outputs. Transparency, accountability, and human oversight are essential in deploying these technologies responsibly.
Fear of AI becoming superintelligent and posing an existential threat
Current AI systems, including LLMs, are narrow and specialized, without the general intelligence or self-awareness required for superintelligence. However, long-term research into AI alignment and safety is essential.
Fear of AI enabling mass surveillance and privacy violations
Privacy concerns are valid, and strict data governance and privacy-preserving AI techniques must be employed. Regulatory frameworks and public discourse are needed to strike the right balance.
Fear of AI exacerbating social inequalities and concentrating power
Gen AI should be developed and deployed inclusively to benefit all segments of society. Diverse perspectives and democratic governance are crucial to prevent AI from amplifying existing biases or power imbalances.
References:
Bauer, G., Leyva, L., Maramba, I., Marsh, A., & Yeary, M. (2022). Ethical considerations in the use of AI for mental health care. Nature Medicine, 28, 1-4.
Gaudiano, B. A., & Siev, J. (2023). Artificial intelligence in mental health: Current applications and future directions. Behavior Therapy, 54(2), 316-330.
Karras, K., Laine, S., & Aila, T. (2021). Towards artificial general intelligence with hybrid models.
Mahoney, J. S., Craven, J. L., & Gross, G. M. (2020). Clinical ethics and the therapeutic relationship: Beyond the principle of autonomy. Psychotherapy, 57(4), 487-495.
Norcross, J. C. (Ed.). (2010). Evidence-based therapy relationships. National Register of Health Service Psychologists.
Palanica, A., Thommandram, A., Wei, C., Li, Y., & Fossat, Y. (2021). Natural language processing for mental health applications: A sociotechnical perspective.
Rollins, J. C., Raskin, M. A., Wissow, L. S., Lueking, B. S., & Mitchell, J. N. (2022). The future of primary care mental health: Integrated practices and digital technologies. Journal of Primary Care & Community Health, 13, 21501319221100762.
Schwalbe, N., Lepping, P., Christian, C., & Wand, A. P. (2020). Understanding boundaries in a changing mental health system: Staff perspectives in integrated primary mental health services. BMC Health Services Research, 20(1), 1-13.
Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019). Chatbots and conversational agents in mental health: A review of the psychiatric landscape. The Canadian Journal of Psychiatry, 64(7), 456-464.
Wampold, B. E. (2015). How important are the common factors in psychotherapy? An update. World Psychiatry, 14(3), 270-277.
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