When machines explain medicine: Nursing ethics and clinical communication

Area of Science:

• Medical Ethics
• Health Informatics
• Clinical Communication

Background:

• Clinical communication is vital for patient understanding and trust.
• Large language models (LLMs) are increasingly used to simplify technical medical reports for patients.
• The ethical implications of LLM-driven explanations in clinical settings, especially nursing, are not well understood.

Purpose of the Study:

• To perform a normative ethical analysis of how LLM-mediated explanations impact clinical communication.
• To examine the redistribution of interpretive authority among stakeholders (clinicians, nurses, patients, algorithms).
• To assess the effects on responsibility, accountability, and the communication of uncertainty and trust.

Main Methods:

• Normative ethical analysis.
• Informed by peer-reviewed empirical studies (2022-2025) on LLM-assisted explanations in radiology and pathology.
• Focused, non-systematic literature search of biomedical databases.

Main Results:

• LLM-assisted explanations reshape clinical meaning-making, especially with complex information and uncertainty.
• Interpretive framing increasingly occurs outside direct clinician-patient dialogue.
• Interpretive authority is distributed across clinicians, nurses, patients, and systems, but accountability remains with professionals.

Conclusions:

• LLM-assisted explanation acts as mediation, not neutral translation.
• This mediation raises significant ethical concerns regarding interpretive authority.
• Ethical considerations include responsibility, trust, and the representation of uncertainty in LLM-influenced clinical communication.