How do eye-care nurses decide when to trust generative AI?
Interviews with 26 ophthalmic nurses in China found neither blanket acceptance nor rejection. Nurses described checking AI task by task, protecting professional boundaries and relying on organisational support—useful design evidence, but not a test of patient safety or clinical performance.
The short version
- What happened
- Researchers conducted semi-structured critical-incident interviews with 26 ophthalmic nurses recruited for variation across primary, secondary and tertiary eye-care settings in eastern, central and western China.
- Who it affects
- Nurses and clinical leaders need task-level rules, reliable escalation and time to check AI output—especially where an error could affect medication or an irreversible visual outcome.
- What remains unproven
- The sample comprises 26 ophthalmic nurses and cannot estimate how common each view is.
Keep in mind
The sample comprises 26 ophthalmic nurses and cannot estimate how common each view is.
Interviews capture reported experience rather than observed behaviour or patient outcomes.
No specific generative-AI product, diagnostic comparison or error rate was tested.
Transferability beyond Chinese ophthalmic settings requires further study.
The study was supported by two Chinese military health and nursing-education grants; the authors declared no competing interests.
Source analysis · 1 linked source. Primary analysed source date: 21 October 2026.
Read the full report · 3 min The evidence, methods, implications and limitations
Trust changed with the task rather than moving in one direction
The researchers used maximum-variation sampling and interviewed 26 ophthalmic nurses from different levels of care across three broad Chinese regions. Critical-incident prompts asked participants to describe concrete encounters, and transcripts were analysed with reflexive thematic analysis following the COREQ reporting framework. Nurses described beginning with curiosity, checking whether an AI appeared competent and then granting only conditional, task-specific trust.[1]
Professional responsibility and patient relationships set limits
The three themes covered tentative competence appraisal, protection of professional boundaries and trust within an organisational ecology. Participants described particular sensitivity to irreversible visual harm, precise medication tasks and the dependence of visually impaired patients on nurse-mediated care. The authors call the resulting pattern “gradient calibration”: trust was adjusted by task, ethical duty, patient relationship and workplace support rather than treated as a yes-or-no attitude.[1]
The distinction that matters
What shaped nurses’ trust
The interviews point to three connected layers rather than a single trust score.
Task
Check competence for the specific use
Conditional reliance
Duty
Protect professional and patient boundaries
Human accountability stays visible
Organisation
Provide rules, training and escalation
Trust can be calibrated in practice
The findings guide implementation questions, not clinical claims
This qualitative design can reveal recurring experiences but cannot estimate prevalence or prove that a trust strategy improves care. The sample is small and specialty-specific, participants' accounts may not transfer outside China, and no named model was evaluated. The immediate implication is practical: hospitals introducing generative AI should define permitted tasks, escalation and accountability, then observe how those rules work in real care rather than assuming staff trust is fixed.[1]