Calibrating trust in generative artificial intelligence for clinical communication through Chinese medical humanism

Generative artificial intelligence is entering clinical communication through patient messaging, health education, and conversational decision support. Its capacity to produce reassuring language can coexist with inaccurate advice, unclear responsibility, and culturally inappropriate explanations. This mini-review examines how Chinese medical humanism can inform the calibration of trust in these encounters. Targeted literature identification integrates studies of trust and perceived empathy, research on traditional Chinese medicine communication, and medical education evidence with normative scholarship. The evidence distinguishes perceived empathy from relational accountability and technical performance from demonstrated patient benefit. Chinese medical ideals of benevolence, sincerity, holistic attention, and contextual judgment offer resources for interpreting professional responsibilities, but their incorporation into artificial intelligence has not been shown to restore trust or improve health outcomes. We synthesize these insights into a proposed dual-verification approach: an epistemic track checks evidence, uncertainty, and safety, while a relational track checks patient understanding, cultural meaning, preferences, and access to human care. Their convergence supports accountable communication and allows unresolved concerns to trigger human review. Educational applications connect critical appraisal of generated responses with reflection on classical medical ethics. Evaluation should prioritize appropriate reliance, error recognition, patient agency, and equitable access rather than maximizing trust scores. This approach reframes humanistic integration as a set of responsibilities for clinicians and institutions, with its effectiveness remaining an empirical question.