AINurse-26 | Fifth International Workshop on AI in Nursing

About the Workshop

From AI Methods to Real-World Nursing Practice

AINurse-26 focuses on a new and emerging frontier in AI research and practice: the implementation and governance of trustworthy AI systems in nursing. Building on the success of four prior AINurse workshops, this edition shifts emphasis from developing AI methods to understanding how existing AI research can be responsibly translated into clinical care.

Nurses — the largest segment of the global healthcare workforce — are central to this transition as primary users, evaluators, and mediators of AI-enabled clinical systems. Yet nurses remain underrepresented in AI research communities focused on deployment and governance.

The workshop will bring together AI researchers, nurses, students, and interdisciplinary stakeholders to examine design principles, evaluation strategies, and organizational challenges that determine whether AI systems achieve meaningful clinical and societal impact.

AINurse-26 addresses the gap between AI innovation and clinical translation, aligning with emerging priorities to ensure that AI delivers real-world value while minimizing unintended harm.

Workshop History

Five Years at AIME

2022

AINurse-22 — AIME 2022 First workshop; AI methods and models in nursing informatics

2023

AINurse-23 — AIME 2023 Advancing clinical decision support and NLP in nursing

2024

AINurse-24 — AIME 2024 Generative AI, large language models, and nursing data

2025

AINurse-25 — AIME 2025 Human-centered AI evaluation and workflow integration

2026

AINurse-26 — AIME 2026 Implementation, governance, and trustworthy AI This Year

Each workshop has attracted 20–40 participants, featured 8–10 accepted presentations, and fostered sustained international collaboration. Outputs include peer-reviewed publications and ongoing interdisciplinary research initiatives.

Call for Papers

Selected Topics

We welcome original research addressing the implementation, governance, and trustworthy deployment of AI in nursing and healthcare settings. Topics include but are not limited to:

Implementation & Deployment

Workflow integration, sociotechnical considerations, and real-world evaluation of AI in nursing practice.

Trustworthy & Ethical AI

Fairness, accountability, transparency, privacy protection, informed consent, and governance frameworks in nursing contexts.

Explainable & Human-Centered AI

Interpretability, usability, and clinician trust in AI-supported decision-making at the point of care.

Clinical Decision Support

Design, evaluation, and implementation of nurse-centered clinical decision support (CDS) systems.

NLP & Generative AI

Responsible use of large language models and text analytics in nursing documentation and clinical reasoning.

Implementation Science & Human Factors

UI/UX design, human-AI interaction, and organizational readiness for AI adoption in clinical environments.

AI for Nursing Education

AI-enabled training, competency development, clinical upskilling, and workforce development initiatives.

Equity & Bias in Nursing AI

Documentation quality, data bias, AI fairness, and equitable outcomes for diverse patient populations.

Workshop Program

Format

AINurse-26 will be held as a full-day, in-person workshop co-located with AIME 2026. The program is designed to maximize interdisciplinary exchange and facilitate concrete research agenda-setting.

Peer-Reviewed Paper Presentations

8–10 accepted papers presented by authors, with Q&A and discussion from the interdisciplinary audience.

Invited Keynote / Panel

An invited keynote speaker or expert panel addressing trustworthy AI implementation in real-world clinical settings.

Structured Panel Discussion

"Implementing Trustworthy AI in Nursing: From Research to Practice" — a focused panel engaging participants in active debate.

Facilitated Closing Discussion

A structured session to identify shared challenges and collaboratively define future research directions in AI and nursing.

Paper Submission

Call for Submissions

Submit a Paper via EasyChair

Paper Submission Deadline

May 6, 2026

Submit via EasyChair

Notification of Acceptance

May 8, 2026

Authors notified by email

Workshop

July 10, 2026

Full day

Topics of Interest

Implementation and deployment of AI in nursing practice: Workflow integration, sociotechnical considerations, and real-world evaluation
Trustworthy and ethical AI in nursing: Fairness, accountability, transparency, privacy protection, consent, and governance
Explainable and human-centered AI: Interpretability, usability, and clinician trust in AI-supported decision-making
Clinical decision support for nursing: Design, evaluation, and implementation of nurse-centered CDS systems
Natural language processing and generative AI: Responsible use of large language models and text analytics in nursing contexts
Implementation science and human factors: UI/UX design, human-AI interaction, and organizational readiness
AI for nursing education and workforce development: AI-enabled training, competency development, and clinical upskilling
Algorithm biases: Detection and elimination of algorithmic biases to improve health equity, including racial and other biases
AI ethics and governance: Ethical frameworks, privacy considerations, and governance models for responsible AI implementation in nursing practice

Submissions

Papers should be submitted to the workshop EasyChair page. The workshop features regular papers in three categories:

Extended Abstract (up to 3 pages) or Demonstration paper (1 page) describing either:

a research project,
a demonstration of implemented systems, or
late-breaking results (work-in-progress).

We are also accepting the following two proposal types:

Panel proposal (up to 2 pages) describing the proposed topic and including the invited moderator and panelists
Debate on topics related to AI and nursing (up to 2 pages) describing the proposed topic and including the invited moderator and panelists

Submissions should be formatted according to Springer's LNCS format (Word template is also available here). Authors can include an appendix that does not count towards the page limit, however there is no guarantee that reviewers will read it. Springer's proceedings LaTeX templates are also available in Overleaf. Springer encourages authors to include their ORCIDs in their papers. Selected accepted abstracts will be invited to submit their full paper to a special journal issue (to be announced).

Organizing Committee

Workshop Chairs

All chairs are co-founders of the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative and co-chairs of the prior four AINurse workshops.

Martin Michalowski

PhD, FAMIA, FIAHSI

School of Nursing Foundation Research Professor & Co-Director, Center for Nursing Informatics · University of Minnesota, USA

Research in AI methods, information integration, record linkage, LLMs, and clinical decision support. Co-President, AIME Society. Organizer, W3PHIAI at AAAI. Program Committee Chair, AINurse-26.

Laura-Maria Peltonen

PhD, MNSc, RN, FEANS, FIAHSI

Associate Professor, Department of Health and Social Management · University of Eastern Finland & Kuopio University Hospital, Finland

Research at the intersection of nursing science, digital health, and AI, with focus on clinical decision-making, workforce management, and patient outcomes. Chair, EFMI Nursing Informatics Working Group.

Lisiane Pruinelli

PhD, MS, RN, FAMIA, FAAN

Interim Chair, Dept. of Biobehavioural Nursing Science & Associate Professor · University of Florida, Gainesville, FL, USA

Expertise in applied clinical informatics and AI in transplantation. Co-Chair, Nursing Knowledge Big Data Science Initiative. Steering Committee, UF Learning Health System Program.

Charlene Ronquillo

PhD, RN

Lead, Health Informatics Equity Lab · University of British Columbia Okanagan School of Nursing, Canada

Research on health equity, inclusive technologies, and AI fairness and trustworthiness. Focused on clinical documentation, data quality, and equitable AI. President-elect, Canadian Nursing Informatics Association.

Maxim (Max) Topaz

PhD, RN, MA, FIAHSI, FACMI

Elizabeth Standish Gill Associate Professor of Nursing · Columbia University Medical Center, New York, USA

Pioneer in applying NLP to nursing-generated data. Research on data science for clinical decision-making. Affiliated with the Columbia University Data Science Institute. Author of 170+ articles.

Who Should Attend

Target Audience

AINurse-26 is intended for researchers, clinicians, and students involved in developing, evaluating, or implementing AI in nursing and healthcare. We anticipate 20–40 participants consistent with prior AINurse workshops.

Clinical Professionals

Nurses, physicians, and allied health professionals working with AI-enabled clinical systems

AI & Data Scientists

Researchers in machine learning, NLP, and data science with interest in healthcare applications

Implementation Specialists

Human factors engineers, ethicists, and implementation scientists focused on health AI

NAIL

NAIL Collaborative

Nursing and Artificial Intelligence Leadership

Organizing Body

About the NAIL Collaborative

The Nursing and Artificial Intelligence Leadership (NAIL) Collaborative is an international organization focused on promoting the use of AI to support nursing care and advance patient outcomes. All AINurse workshop chairs are co-founders of NAIL.

NAIL members have organized numerous conference panels, presentations, and events, including sessions at the Canadian Nursing Informatics Association, International Medical Informatics Association, AMIA Nursing Working Group, Alliance for Nursing Informatics, MIE2025, MedInfo 2025, and AIME 2020, 2022, and 2025.

Outputs from prior AINurse workshops include peer-reviewed publications and ongoing interdisciplinary research initiatives spanning multiple countries and institutions.

Fifth International Workshop on
Artificial Intelligence in Nursing