Other Titles

The Safeguard that has not been built yet. Accelerating Nursing Science with AI: Promise, Risk, and The Path Forward [Title Slide]

Other Titles

International Nurse Researcher Hall of Fame Recipient Presentation

Abstract

AI is transforming nursing at unprecedented speed, but our institutions lack safeguards to match. This Hall of Fame address presents five projects spanning bibliometric analysis, living systematic reviews, multimodal clinical AI, fabricated citation detection, and clinical deskilling. Actionable frameworks for preserving clinical competence are presented.

Notes

Presenter notes available in attached slide deck. To see the notes in Adobe Acrobat, go to Tools > Comment or look at your Layers Panel. If the notes were saved as comments or layers, you can toggle them visible.

References:

1. Topaz, M., Roguin, N., Gupta, P., Zhang, Z., & Peltonen, L.-M. (2026). Fabricated citations: An audit across 2.5 million biomedical papers. The Lancet, 407, 1779-1781.

2. Topaz, M., Peltonen, L. M., Zhang, Z., & Backonja, U. (2026). The deskilling effect: Is AI eroding clinical competence? Annals of Internal Medicine, 179.

3. Zhang, Z., Gupta, P., Song, J., et al. (2025). Automated extraction of health problems and nursing interventions using LLMs. Journal of Nursing Scholarship.

4. Song, J., Beigi, H., Davoudi, A., et al. (2022). Capturing patient-clinician communication during home healthcare visits. JAMIA Open, 5(3), ooac069.

5. Topaz, M., Peltonen, L. M., & Zhang, Z. (2025). Beyond human ears: AI scribes in clinical documentation. npj Digital Medicine, 8, 465.

6. Lipschuetz, M., & Topaz, M. (2026). Prioritizing nurse-patient relationships in digital transformation. International Journal of Nursing Studies, 177, 105369.

7. Michalowski, M., Topaz, M., & Peltonen, L. M. (2025). An AI-enabled nursing future with no documentation burden. Journal of Advanced Nursing.

Description

Sigma celebrates 2026 International Nurse Researcher Hall of Fame Inductees 

Twenty-six nurse researchers join the ranks of the profession's most transformative voices 

This year marks the 17th presentation of this prestigious honor. The 2026 inductees join more than 300 of the most distinguished nurse researchers globally, whose work has left a lasting mark on the science, practice, and policy of nursing for generations to come. 

This slide deck represents the presentation of a current inductee. 

Author Details

Maxim (Max) Topaz, PhD, RN, MA, FAAN, FIAHSI, FACMI

Dr. Topaz is the Elizabeth Standish Gill Associate Professor of Nursing at Columbia University and Columbia Data Science Institute. His work develops and evaluates AI methods, including natural language processing and multimodal models using text, speech, and video, to predict patient deterioration, support clinical decisions, and reduce documentation burden for frontline nurses. He has published 200+ articles and secured 25M+ in federal funding.

Sigma Membership

Xi

Type

Presentation

Format Type

Text-based Document

Study Design/Type

Expert Opinion (nationally/internationally recognized)

Research Approach

Other

Keywords:

Artificial Intelligence, Generative Artificial Intelligence, Nursing Research, Big Data, Acquisition of Data, Data Processing, Technology Risk Assessment, Artificial Intelligence & Ethics

Conference Name

37th International Nursing Research Congress

Conference Host

Sigma Theta Tau International

Conference Location

Toronto, Ontario, Canada

Conference Year

2026

Rights Holder

All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.

Review Type

Abstract Review Only: Reviewed by Event Host

Acquisition

Proxy-submission

Date of Issue

2026-10-05

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Accelerating Nursing Science With AI: Promise, Risk, and the Path Forward

Toronto, Ontario, Canada

AI is transforming nursing at unprecedented speed, but our institutions lack safeguards to match. This Hall of Fame address presents five projects spanning bibliometric analysis, living systematic reviews, multimodal clinical AI, fabricated citation detection, and clinical deskilling. Actionable frameworks for preserving clinical competence are presented.