Navigating Artificial Intelligence in Neuroregulation Practice: Ethical Principles, Risk Stratification, and a Clinical Policy Framework

Abstract

Artificial intelligence (AI) tools are entering neuroregulation practice through multiple simultaneous pathways, including automated qEEG analysis, protocol recommendation systems, AI-assisted documentation, and consumer-facing mental health applications that clients bring directly into the therapeutic relationship. No ethics code specific to neuroregulation has yet addressed these applications directly. This article presents a conceptual and practical ethical framework grounded in established codes, including the BCIA Code of Ethics, ISNR Code of Ethics, APA Ethical Principles, ACA Code of Ethics, and the APA (2025) Ethical Guidance for Artificial Intelligence. A risk continuum model organizing AI applications along three dimensions (opacity, clinical consequence, and distance from oversight) is proposed. Four ethical domains are addressed: informed consent and transparency, practitioner competence and scope, data privacy and vendor accountability, and three AI accuracy risks (hallucination, automation bias, and collusion). A five-element practice policy framework is presented in tabular form for direct clinical use. Existing ethics principles are sufficient to guide responsible AI integration when applied deliberately. The practitioner remains accountable for AI-assisted decisions regardless of tool design or vendor claims. Proactive policy development positions neuroregulation clinicians to shape the field's emerging standards.