Ethical Responsibility in the Off-Label Use of AI in Medical Imaging

Clinical Snapshot

Key Findings

• Effect Size: Not applicable - theoretical framework

• Primary Outcome: Development of future-looking collective responsibility framework for off-label AI use in medical imaging

• Nnt Or Sensitivity: Framework applicability across multiple stakeholder groups including patients, clinicians, healthcare organizations, and AI developers

Clinical Application

High feasibility for implementation in structured healthcare environments with established governance frameworks Highly relevant to Australian practice given TGA regulation of AI medical devices, RACGP digital health guidelines, and emphasis on patient-centered care in Australian healthcare policy Healthcare professionals, healthcare organizations, and patients involved in AI-assisted medical imaging across all clinical specialties

Abstract

AbstractArtificial intelligence in medical imaging (AI-MI), categorized by the U.S. Food and Drug Administration (FDA) as "Software as a Medical Device," can offer significant benefit to medical care. FDA approval-represented by a "label"-indicates that the device has demonstrated safety and efficacy for a specific clinical task and population for which the device is intended. Off-label use of AI-MI outside of the task and/or population can be medically and ethically problematic, yet benefits may be reduced or missed if entirely prohibited. Ethical responsibility frameworks can assist in evaluating and establishing boundaries and opportunities in off-label use of AI-MI. In this work, we review three different models for ethical responsibility and apply them to off-label use of AI-MI. We find that a future-looking collective responsibility framework, involving stakeholders such as industry developers, healthcare organizations, clinicians, and, most importantly, patients and/or their representatives, promotes proactive and practical actions to help ensure safe and ethical use of AI-MI off-label. By adopting this approach, stakeholders can balance innovation with safeguards, advancing patient-centered care while mitigating potential harms associated with off-label AI-MI use. This framework is crucial as AI-MI becomes increasingly integral to clinical decision-making processes.

References

1. 1.Whitney, H. M., Chin, M. H., & Giger, M. L. (2026). Ethical responsibility in the off-label use of AI in medical imaging. The Journal of Clinical Ethics, 37(1). https://doi.org/10.1086/740421