Nursing AI groups present five papers at AAAI health symposium

The Global Nursing AI Alliance and the American Board of Nursing Artificial Intelligence presented five papers at AAAI’s inaugural health and nursing workforce symposium in Arlington, Virginia. The work focused on nursing AI competency, workforce readiness, education, and how clinicians should oversee AI in care settings.

Why it matters: - Nursing groups are trying to define how nurses should evaluate, govern, and use clinical AI before those tools become routine in care. - The papers focus on patient safety, clinical oversight, and workforce preparation, not just technical performance. - The symposium work also signals that nursing expertise is becoming part of broader AI governance discussions in health care.

What happened: - The Global Nursing AI Alliance and the American Board of Nursing Artificial Intelligence presented five papers at the inaugural Agentic and Trustworthy AI for Health and the Global AI-Ready Nursing Workforce symposium. - The symposium took place Nov. 5-7 at the Westin Arlington Gateway in Arlington, Virginia, as part of the Association for the Advancement of Artificial Intelligence’s 2026 Fall Symposium Series. - Organizers reported that 23 papers were accepted and presented. - Vanessa Riley, founder and president of the Global Nursing AI Alliance, co-chaired the symposium with Hong Qin of Old Dominion University, Apurva Narayan of Western University, and Catherine Fang of Carnegie Mellon University. - Simmy King, president of ABNAI, served on the organizing team. - Delaney W. La Rosa also took part in the series’ plenary session for symposium summaries.

The details: - Delaney W. La Rosa presented “Artificial Intelligence Competencies in Nursing: A Narrative Review of Current Evidence, Frameworks, and Gaps,” co-authored with Jennifer Rossetti, Sally Carlisle, Vanessa Riley, and Simmy King. - The paper mapped the literature to ABNAI’s seven competency domains. - The authors identified gaps in validated measures of applied AI skills, including equity-related competencies. - The symposium agenda linked those competencies to nursing responsibilities from system design through deployment and monitoring. - Simmy King, Delaney W. La Rosa, and Vanessa Riley used the ABNAI framework to develop a 105-item self-assessment for an exploratory cross-sectional study. - Riley presented the analysis of responses. - Respondents with prior AI education rated themselves higher than those without it, 2.65 versus 1.80. - Data literacy, analytics, and governance was the lowest-rated domain. - Riley also presented “Building an AI-Ready Global Nursing Workforce: An Interdisciplinary, Expert-Informed Framework.” - The GNAA team combined a targeted literature review with input from 29 contributors across nursing specialties, informatics, data science, and other disciplines. - The framework identified eight themes for workforce readiness, including nursing leadership in AI governance, accountability for clinical decisions, and adaptation to local care settings and resources. - La Rosa presented two additional papers. - One introduced the AI Discernment Framework for Teaching and Learning. - The other, co-authored with Beth McManus and Jennifer Rossetti, examined graduate nursing students’ research proficiency after the introduction of an AI research assistant. - In a session chaired by Riley, Zhentao Fan presented work with Zhangfei Yang and Yuheng Zhao that evaluated four safeguards across nine reviewers in a nurse-facing patient deterioration testbed. - Aizierjiang Aiersilan proposed a standard of care for nurse- and physician-facing AI centered on actual clinician review of consequential AI outputs. - Sharonda Davis, RN, GNAA’s co-director of Acute Care and Surgery, co-authored the workforce framework and served on the symposium organizing committee.

Between the lines: - The presentations frame nursing as an active decision-maker in AI procurement, deployment, and monitoring rather than a passive end user. - The emphasis on governance, local context, and clinician review suggests concern that AI tools can fail if they are not matched to real workflows and patient needs. - The low self-ratings in data literacy, analytics, and governance point to training gaps that could slow safe adoption. - The symposium brought together nurses, data scientists, and computer scientists, which reflects a push for cross-disciplinary standards rather than isolated nursing guidance.

What’s next: - The GNAA and ABNAI frameworks are positioned to guide future nursing AI education, competency testing, and workforce development. - The self-assessment results and competency mappings could support new validated measures of nursing AI skill. - The oversight papers may inform how hospitals test whether AI recommendations fit the care setting, patient condition, language needs, and care preferences. - Riley said nurses should help shape clinical AI decisions from the beginning and continue evaluating systems after implementation.