When machines explain medicine: Nursing ethics and clinical communication.
Authors
Affiliations (4)
Affiliations (4)
- Department of Thoracic Surgery, Guizhou Provincial People's Hospital, Guiyang, China.
- NHC Key Laboratory of Pulmonary Immunological Diseases, Guizhou Provincial People's Hospital, Guiyang, China.
- Department of Cardio-Thoracic Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
- College of Traditional Chinese Medicine, Hainan Medical University, Haikou, China.
Abstract
Clinical communication is ethically consequential, shaping how diagnostic information is interpreted, trusted, and acted upon. Large language models (LLMs) are increasingly used to reformulate technical reports into patient-facing explanations. Although such 'plain-language' outputs can improve comprehension and reduce communication workload, their ethical implications for everyday clinical communication, particularly in nursing practice, remain underexamined. This article provides a normative ethical analysis of how LLM-mediated explanations reshape clinical communication. It addresses three interrelated questions: how automated explanation redistributes interpretive authority among clinicians, nurses, patients, and algorithmic systems; how this redistribution affects responsibility and accountability; and how LLM-mediated explanations influence the ethical communication of uncertainty and trust in routine care. The analysis is informed by peer-reviewed empirical studies published between 2022 and 2025 on LLM-assisted explanation in radiology and pathology communication, identified through a focused, non-systematic search of major biomedical databases. Drawing on this literature, the article examines how automated explanation reshapes clinical meaning-making in contexts characterised by informational complexity and diagnostic uncertainty. Across existing studies, a consistent pattern emerges: as explanation becomes partially automated and more widely accessible, interpretive framing increasingly occurs outside clinician-patient dialogue, including through workflow-integrated tools and patient-side consultation. Interpretive authority is thus distributed across clinicians, nurses, patients, and platform-based systems, while formal accountability remains concentrated within professional roles. We argue that LLM-assisted explanation should be understood as mediation rather than neutral translation, raising important ethical questions about interpretive authority, responsibility, and the representation of uncertainty in clinical communication.