Pillay Y. (2025).
Healthcare (Basel, Switzerland), 13(23), 3057.
Abstract
The meteoric rise in generative AI has created both opportunities and ethical challenges for the mental health disciplines, namely in clinical mental health counseling, psychology, psychiatry, and social work. While these disciplines have been grounded in well-established ethical principles such as autonomy, beneficence, justice, fidelity, and confidentiality, the exponential ubiquity of AI in society has rendered mental health professionals unsure as to how to navigate ethical decision making in the AI era. The author proposes a preliminary ethical framework which synthesizes the code of ethics of the American Counseling Association (ACA), the American Psychological Association (APA), the American Medical Association (AMA), and the National Association of Social Workers (NASW), which is then organized around five pillars: (i) autonomy and informed consent; (ii) beneficence and non-malfeasance; (iii) confidentiality, privacy, and transparency; (iv) justice, fairness and inclusiveness; and (v) fidelity, professional integrity, and accountability. These pillars are juxtaposed with AI ethical guidelines developed by multinational organizations, governmental and non-governmental entities, and technology corporations. The resulting integrated ethical framework provides a practical cogent structure that mental health professionals can use when navigating this uncharted terrain. A case study based on the proposed ethical framework and strategies that clinical mental professionals can consider prior to incorporating AI into their clinical repertoire are offered. Limitations of the framework and its implications for future research are addressed.
Here are some thoughts.
This article proposes an ethical decision-making framework for mental health clinicians integrating generative AI into practice, synthesizing codes from ACA, APA, AMA, and NASW with AI guidelines from global organizations. The framework organizes around five pillars: autonomy and informed consent, beneficence and non-malfeasance, confidentiality and transparency, justice and inclusiveness, and fidelity and accountability. A case study illustrates the framework's application with a refugee client using an AI chatbot as a between-session tool. The author also provides a practical checklist for clinicians before adopting AI, covering consent, vendor vetting, data security, competence, inclusivity, and transparency. Limitations include the framework's untested status, Western-centric bias, and rapid AI evolution, with recommendations for further empirical validation and curriculum updates.
