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# automation bias

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  1. NeuroRegulation

    Sherlin and Longo propose an AI ethics framework for neuroregulation practice, pairing a three-dimension risk continuum of opacity, clinical consequence, and distance from oversight with a five-element clinical policy table

    Addressing AI tools 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, and noting that no ethics code specific to neuroregulation has yet addressed these applications directly, Sherlin and Longo present a conceptual and practical ethical framework grounded in 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, comprising a risk continuum model organizing AI applications along three dimensions of opacity, clinical consequence, and distance from oversight, four ethic

  2. Who Read It First? Documenting Independent Judgment in AI-Assisted Radiology

    This article argues that radiology should treat sequence as a design variable in routine clinical practice: capture what the radiologist concluded before AI exposure, then what the AI displayed and how the radiologist responded, thereby preserving the pre-AI interpretation as an auditable event, while noting that responsibility remains with the radiologist who signs the report and that the proposal is bounded by what each device is cleared to do.

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