At the end of July, I spent five days on the NIH campus attending the Summer Omics Nursing Institute (SONI). A cohort of 61 nurse faculty, students, and practitioners enjoyed a week immersed in state-of-the-art methods and their application to precision health and symptom science (National Cancer Institute, 2026). Inspirational and enriching as the experience was, I couldn't help but wonder: Who will teach this to the rest of nursing?
The first calls for the inclusion of genomics content in nursing curricula occurred over 60 years ago (Seibert, 2020). Since then, despite establishing the essential genomic nursing competencies in 2005 (Jenkins & Calzone, 2007), we haven't made nearly enough progress toward understanding or implementing this knowledge. Calzone et al. (2024) report that the integration of the competencies into nursing curricula remains highly uneven, that continuing education in genomics for nurses is limited, and that the genomic capacity of the nursing workforce remains constrained. Commensurately, genomic literacy scores among practicing nurses, students, and faculty remain low (Dante et al., 2025). A lack of genomic knowledge and lack of confidence in its application have been identified as primary barriers to implementing genomic information in patient care (Hines-Dowell et al., 2024). Although we have known about this gap in genomics knowledge among the nursing workforce for decades, we have nevertheless failed to close it.
With the advent of AI chatbots purposefully designed to help patients understand their own personal genomics and obtain consent for genetic testing, we risk abdicating our expert role as patient educators and translators of medical information (Siglen et al., 2025; Nazareth et al., 2021). We advocate for human interaction to validate chatbot outputs, but can nurses reliably meet that need? If nurses do not understand the foundational genomics and artificial intelligence technology which underpins these clinical tools, what role do we serve in the delivery of precision healthcare?
These tools represent a clear step forward for patient autonomy and precision health, but they are not infallible. Further, AI cannot replicate the human element of care which is characteristic of nursing. The modern nurse needs AI literacy just as much as genomic literacy. Our patients are counting on us to close these gaps in our education and training.
While the literature on AI literacy in nursing is nascent and rapidly evolving, there already exist several published frameworks to help guide the development of AI literacy and competency among nurses (Hoelscher & Pugh, 2025; Kobeissi et al., 2025).
Gaps in nurses’ understanding of AI and genomics also have important implications for healthcare equity. Genomic reference datasets substantially underrepresent non-European ancestries, which produces higher rates of uncertain and misclassified variant results in the populations already least well served by our health systems (Sirugo et al., 2019). Building an algorithmic tool on that foundation risks exacerbating errors and perpetuating biases if outputs are not properly contextualized. In the 2026 AI in Nursing Practice Think Tank, the American Nurses Association identified algorithmic bias in AI tools with the potential to worsen patient safety risk and health disparities. They also note the lack of nursing-specific governance for evaluating AI tools. If asked to use an AI tool in the workplace, a nurse who is fluent in neither genomics nor AI cannot confidently catch a wrong output.
Nurses need to be present when these new technological tools are designed, validated, and regulated (Demiris et al., 2026). We cannot merely be trained on them after deployment. The ANA (2026) named advancing AI literacy and competence and sustaining cross-sector collaboration among its five near-term priorities. Academic–practice partnerships and direct engagement with industry are essential. But we do not get to be in those rooms just because we care. We get there based on our expertise. Building that capacity starts with each of us.
In closing, I recommend to my fellow graduate students: Be intentional seeking out workshops, institutes, and classes which fill these gaps and share what you learn with your colleagues. Consider institutes like SONI, Genomics for Social Scientists (GeSS) or the Bruce Weir Summer Institute in Statistical Genetics. You might also take a cognate in a topic like machine learning, and join an organization like the American Medical Informatics Association (AMIA) or the International Society of Nurses in Genetics (ISONG). We are the largest segment of the healthcare workforce. The technologies which shape precision healthcare will be implemented with us or without us. It's time to earn our seat at the table.
References
American Nurses Association. (2026). Artificial intelligence in nursing practice: Consensus findings from the ANA AI in Nursing Practice Think Tank. https://brand.ana.org/s/2wrfjrh9xqm7bk67mmgfgspn
Calzone, K. A., Stokes, L., Peterson, C., & Badzek, L. (2024). Update to the essential genomic nursing competencies and outcome indicators. Journal of Nursing Scholarship, 56(5), 729–741. https://doi.org/10.1111/jnu.12993
Dante, A., Masotta, V., Paoli, I., Caponnetto, V., Caples, M., Laaksonen, M., Kamenšek, T., Petrucci, C., & Lancia, L. (2025). Genomic literacy in nursing: A systematic scoping review of the literature. BMJ Open. https://doi.org/10.1136/bmjopen-2025-100054
Demiris, G., Oh, O., Ulrich, C. M., Bin You, S., Cho, H., & Villarruel, A. M. (2026). Artificial intelligence and nursing science: Opportunities, challenges, implications, and guidelines. Nursing Outlook, 74(3), 102770. https://doi.org/10.1016/j.outlook.2026.102770
Hines-Dowell, S., McNamara, E., Mostafavi, R., Taylor, L., Harrison, L., McGee, R. B., Blake, A. K., Lewis, S., Perrino, M., Mandrell, B., & Nichols, K. E. (2024). Genomes for nurses: Understanding and overcoming barriers to nurses utilizing genomics. Journal of Pediatric Hematology/Oncology Nursing, 41(2), 140–147. https://doi.org/10.1177/27527530231214540
Hoelscher, S. H., & Pugh, A. (2025). N.U.R.S.E.S. embracing artificial intelligence: A guide to artificial intelligence literacy for the nursing profession. Nursing Outlook, 73(4). https://doi.org/10.1016/j.outlook.2025.102466
Jenkins, J., & Calzone, K. A. (2007). Establishing the essential nursing competencies for genetics and genomics. Journal of Nursing Scholarship, 39(1), 10–16. https://doi.org/10.1111/j.1547-5069.2007.00137.x
Kobeissi, M. M., Maria, D. M. S., & Park, J. I. (2025). Artificial intelligence 101: Building literacy with the AI-ABCs framework. Nursing Outlook, 73(4). https://doi.org/10.1016/j.outlook.2025.102445
National Cancer Institute. (2026). Summer Omics Nursing Institute [Overview]. https://events.cancer.gov/ccr/summer-omics-nursing-institute
Nazareth, S., Nussbaum, R. L., Siglen, E., & Wicklund, C. A. (2021). Chatbots & artificial intelligence to scale genetic information delivery. Journal of Genetic Counseling, 30(1), 7–10. https://doi.org/10.1002/jgc4.1359
Seibert, D. (2020). Genomics in nursing education. Journal of the American Association of Nurse Practitioners, 32(12), 785. https://doi.org/10.1097/JXX.0000000000000529
Siglen, E., Vetti, H. H., Lyssand, A., Dahl-Michelsen, T., & Bjorvatn, C. (2025). Patients' and healthcare professionals' experiences with implementing the Rosa chatbot in mainstream genetic testing for hereditary breast and ovarian cancer. Journal of Genetic Counseling, 34(5), e70119. https://doi.org/10.1002/jgc4.70119
Sirugo, G., Williams, S. M., & Tishkoff, S. A. (2019). The missing diversity in human genetic studies. Cell, 177(1), 26–31. https://doi.org/10.1016/j.cell.2019.02.048