Trustworthy Evidence Communication in AI Healthcare: A Nurse Educator Perspective
Author: Jenny Chua1,2
1. JBI Singapore National University Hospital Nursing Centre
2. National University Hospital, National University Cancer Institute, Singapore
As a nurse educator, I have always believed that communication sits at the heart of healthcare. This is not simply about sharing information, but about ensuring that what we share is accurate, relevant, and meaningful for patient care.
As artificial intelligence (AI) becomes increasingly integrated into our daily lives, more nursing students, practicing nurses, and patients are turning to AI to seek information, support learning, and make sense of health-related questions. This presents an important opportunity, but also a shared responsibility: how do we ensure that the evidence guiding these conversations remains trustworthy?
Through my experiences, I have come to appreciate both the potential and the limitations of AI. Rather than viewing AI as a tool used by some and managed by others, I see students, nurses, and patients as active partners in shaping how AI supports evidence communication. Each group brings unique perspectives, experiences, and responsibilities in ensuring that information is interpreted thoughtfully and applied safely.
By working together and approaching AI with curiosity, critical thinking, and a commitment to evidence-based practice, we can harness its benefits while centring trust in healthcare communication.
Teaching Students: When Impressive Isn’t Always Insightful
Not long ago, I was reviewing a mid-term oncology case presentation with a group of Year 2 nursing students.
At first glance, their work was outstanding. The slides were polished, visually engaging, and filled with detailed nursing interventions, chemotherapy side effects, and patient education points. It was clear they had used AI to help generate their content, and the downside of that soon showed.
When I asked a simple question – ‘How do these interventions relate specifically to your patient?’ – there was total silence in the room.
All five students struggled. About half of the interventions couldn’t be justified, and the chemotherapy side effects listed didn’t match the patient’s actual experience. Interestingly, the patient education section was fairly accurate, but it remained general, not personalised.
That moment was eye-opening. It reminded me that while AI is incredibly efficient at generating information, it doesn’t replace the critical thinking and clinical reasoning our students need to develop.
Since then, I’ve started emphasising a different approach, where I tell my students that AI is a starting point, not the final answer.
I now encourage students to:
- Cross-check AI-generated content with the evidence-based literature
- Verify information against institutional guidelines
- Evaluate whether it truly applies to their patient
- Ask questions instead of accepting content at face value.
Moving forward, I’m also changing how I teach. I’m setting clearer expectations around AI use, requiring students to explain their rationale for every intervention, and incorporating more reflective, reasoning-based questions. Perhaps most importantly, I’m integrating AI literacy alongside evidence-based practice.
Supporting Nurses: Balancing Efficiency with Responsibility
AI is now increasingly becoming part of clinical practice. For nurses, this brings both convenience and risk.
I encountered a patient who asked a nurse if they could take health supplements during their treatment. The nurse quickly checked an AI tool for the answer. It’s fast, accessible, and seems helpful. However, AI doesn’t always guarantee accuracy and relevance. Although AI suggested that the health supplement could be taken, it did not consider the ingredients in the health supplement that cause a contraindication with the patient’s treatment.
That’s why it’s essential for nurses to:
- Verify AI-generated content using reliable sources
- Check institutional policies and clinical guidelines
- Ensure information is aligned with current best practices
- Use their clinical judgement before sharing advice.
In the end, AI should support, not shortcut, the decision-making process.
Guiding Patients: When Information Becomes Overwhelming
Perhaps the most striking impact of AI I’ve seen is among patients.
Recently, I encountered two patients who shared how AI influenced their healthcare journeys.
One patient shared that, initially, they attended a clinic seeking treatment for their persistent gout problem. When their symptoms did not improve, AI suggested that they undergo a comprehensive health screening. After receiving their blood test results, they entered the results into an AI platform, which advised them to seek an immediate hospital review.
Another patient experiencing unresolved lower back pain was similarly advised by AI to undergo bloodwork investigations.
Both patients were eventually diagnosed with the same condition after being found to have elevated white blood cell counts. However, despite sharing a diagnosis, their treatment plans differed considerably based on their individual clinical needs.
What followed during chemotherapy was telling. Both patients asked detailed and persistent questions – about the drugs, treatment duration, side effects, and even where the medications came from, because the information they had received from AI didn’t match what the healthcare team shared. As a result, nurses had to spend additional time clarifying and verifying information.
These experiences showed me both sides of AI.
On the one hand, it can encourage patients to seek care early, a clear benefit.
Yet, on the other hand, it can create confusion when general information is mistaken for personalised medical advice.
We need to remind nurses that:
- AI provides general information, not personalised medical advice
- Online information should always be discussed with healthcare professionals
- Trusted hospital resources and evidence-based websites are more reliable
- Not all information online is accurate or applicable.
Ultimately, this is about promoting health literacy, which means helping patients understand not just what information they see, but how to interpret it.
Final Reflections: Working with AI and Learning from One Another
AI has the potential to transform healthcare education, support clinical practice, and help patients better understand their health. However, AI is only as valuable as the way we use it. Human expertise, professional judgement, and critical thinking remain essential to ensure that information is accurate, relevant, and applied appropriately to meet each individual’s needs. Rather than replacing healthcare professionals, AI should be seen as a tool that helps us learn, make informed decisions, and deliver better care.
At its heart, trustworthy healthcare communication is not about humans versus AI – it is about people and technology working together. Students, nurses, and patients all have an important role to play by asking questions, checking information, and engaging in meaningful conversations. When we combine the strengths of human experience with the capabilities of AI, we can foster safer, more informed, and collaborative care while building greater confidence in the evidence and information being shared.
References
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095
Glauberman, G., Ito-Fujita, A., Katz, S., & Callahan, J. (2023). Artificial intelligence in nursing education: Opportunities and challenges. Hawai'i Journal of Health & Social Welfare, 82(12), 302. https://doi.org/10.62547/yeis2105
Lockey, S., Gillespie, N., Holm, D., & Someh, I. A. (2021). A review of trust in artificial intelligence: Challenges, vulnerabilities and future directions. In T. Bui (Ed.), Proceedings of the 54th Hawaii International Conference on System Sciences (pp. 5463–5472). University of Hawaii at Manoa. https://doi.org/10.24251/HICSS.2021.664
Ronquillo, C. E., Peltonen, L. M., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., Nyrup, R., Rahimi, S., Reed, D. N., Salakoski, T., Salanterä, S., Walton, N., Weber, P., Wiegand, T., & Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of Advanced Nursing, 77(9), 3707–3717. https://doi.org/10.1111/jan.14855
To link to this article - DOI: https://doi.org/10.70253/XHRM2600
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