Ethical AI Use: A Clinical Framework for Professional Practice

The rapid integration of artificial intelligence into mental health practice has generated ethicalconcerns, yet individual practitioners lack clear guidance on responsible AI use. This manuscriptproposes a clinical framework grounding ethical AI interaction in established therapeuticboundary principles—concepts mental health professionals already possess through training andpractice. Drawing from clinical training (therapeutic boundaries, informed consent, scope ofpractice), law enforcement principles (documentation, threat assessment), disability justiceperspectives (accommodation vs. replacement), and cross-domain skill transfer, six coreprinciples emerge: (1) clear role definition, (2) context provision without therapy, (3)professional boundaries, (4) informed consent and data security (including HIPAA compliancefor clinical practice), (5) decision-making authority, and (6) dependency monitoring. This praxis-based framework addresses the literature gap regarding individual practitioner guidance anddisability perspectives in AI ethics. The framework demonstrates that existing professional ethicscodes already provide AI guidance through transferable skills, requiring no entirely newframeworks but rather application of boundary management expertise to technological tools. Aself-assessment tool enables practitioners to evaluate their AI interactions systematically. Thiswork contributes theoretical understanding of how clinical training translates to technologicalcontexts while offering practical implementation guidance for mental health professionalsnavigating AI integration responsibly