In an age of rapidly evolving artificial intelligence, how are nurses ensuring they have a voice in the technology’s development?

By Megan Hinners

More Than Just End Users, Innovators

Artificial intelligence (AI) is rapidly reshaping health care on a broad scale. Algorithms can predict which patients are at risk for deterioration. Generative AI can draft clinical documentation, and ambient listening technologies can capture conversations and translate them into electronic health record documentation. Machine learning can analyze vast data sets and uncover patterns that would be impossible for an individual clinician to detect on their own. AI is increasingly helping health care organizations prioritize clinical interventions, flag potential complications, and support clinical decision-making.

Used thoughtfully, these tools can help care teams recognize patterns hidden within vast amounts of patient data. They can also reduce administrative burden and support more personalized care. However, used carelessly, AI can create risks. Poorly designed systems can reinforce existing biases embedded in data, contribute to alert fatigue, diminish clinical judgment, or erode trust between patients and clinicians.

As AI tools become more advanced and integrated into everyday tasks, nurses have an opportunity to ensure that these technologies strengthen nursing practice rather than undermine it. Thankfully, nursing has never been a profession that shies away from innovation. From Florence Nightingale’s pioneering use of data to improve care outcomes in the 1850s, to modern electronic health records, predictive analytics, remote monitoring systems, and digital communication tools, nurses have consistently adapted to, and helped shape, technological advancements in health care. When it comes to bridging the gap between clinical care and computer technology, nurses have been involved since the technology-driven computer era started taking shape in the late 1950s. Yet, AI presents a new challenge. Because AI systems are only as effective as the people who design, evaluate, and oversee them, nurses have an opportunity to not merely adopt the technology, but to help lead the way in its development.

A Badger Nurse Leading the Way

While much of the public conversation around AI focuses on efficiency, automation, and innovation, for nurses many conversations center around how it will impact clinical judgment and human-centered care. Without nursing input, these tools risk missing critical realities of patient care, introducing bias, disrupting workflows, increasing burnout, or creating recommendations that fail to account for the complexities nurses navigate every day.

As AI becomes more embedded in health care, the UW–Madison School of Nursing is seeing its scholars, scientists, and informaticists lead the conversation with national leaders to advocate that the future of the technology needs nursing.

Helping lead the way in promoting these vital dialogues is UW–Madison School of Nursing alumna and Assistant Professor, Ann Wieben ’12, PhD’23, MS, RN, NI-BC, FAMIA, who is ensuring nursing’s expertise is included in the future of AI.

A nurse scientist, informatics expert, and faculty member at the School of Nursing, Dr. Wieben’s research focuses on how AI and machine learning are integrated into nursing practice. Her work studies how AI tools are designed, integrated into clinical environments, and adopted and evaluated by nurses. She examines the essential question of what happens when emerging technologies intersect with the realities of bedside care.

Dr. Wieben recognized early on in her nursing practice that the expert nurses in the workforce were indispensable to the profession. In a 2023 article titled “What role should artificial intelligence play in nursing,” she wrote, “What impressed me most was their ability to recognize when a patient was experiencing a significant change in status before the rest of us. Their wealth of experience enabled them to recognize patterns in patient data, sometimes in intuitive ways that they couldn’t even articulate themselves.”

She continued to explain in that article that machine learning, which is a branch of AI that focuses on building computer applications that can learn and adapt without being programmed with specific instructions, offers the possibility of extending the pattern recognition that seasoned nurses can bring to the table by leveraging historical patient data to make predictions about the trajectories of current patients.

As a result, Dr. Wieben is exploring the “what ifs” behind AI in nursing. For example, what if there was a way to support newer nurses in recognizing patterns in patient data to enhance their ability to identify changes in a patient’s condition or a patient’s risk for adverse events? For Dr. Wieben, that is the potential that machine learning has in nursing when it is used to drive clinical decision support (CDS) systems.

But realizing that potential requires nurses to be deeply involved in the process, and Dr. Wieben’s research is aiming to amplify nurses’ voices to learn about their experiences with, and perceptions about, AI-driven CDS systems.

A Voice in the Conversation

In one of Dr. Wieben’s most widely cited studies titled “Nurses’ perceptions of the design, implementation, and adoption of machine learning clinical decision support: A descriptive qualitative study,” Dr. Wieben interviewed nurses about machine learning-based CDS systems.

The findings revealed both excitement and caution. Nurses described wanting technologies that improve patient care, provide meaningful insights, and support their work. At the same time, they expressed concerns about becoming overly dependent on technology, losing professional autonomy, and relying too heavily on automated recommendations without applying critical thinking. The research also found that nurses are more likely to trust and adopt AI tools when they know nurses helped design them.

Most importantly, it highlighted a critical reality: nurses possess expertise that technology developers often lack.

Dr. Wieben and her coauthors explained, “Our findings suggest that involving nurses as active members of the design and development teams could enhance model design and adoption. Nurses are the experts on the data they generate, such as patient assessment data. Therefore, their insights into the potential for bias or other issues present in data are crucial for model feature engineering. In terms of fostering adoption, participants in this study reported that they were more likely to be on board with trying these novel tools if the messaging is coming from nurses rather than from non-nurse members of the development team.”

They also noted that these findings align with recommendations from the Nursing and AI Leadership Collaborative (NAIL), which identified nurse engagement throughout AI development as a key priority for successful implementation.

Their findings mirror broader concerns emerging across the profession that nurses must be involved throughout the AI lifecycle, from design and implementation to governance and evaluation. When the American Nurses Association convened its inaugural national AI Think Tank in April 2026, participants representing nursing practice, education, policy, research, and industry identified risks including erosion of professional judgment, unclear accountability when AI influences care decisions, algorithmic bias, loss of patient trust, increased cognitive burden from poorly implemented tools, and the absence of nursing-specific governance standards.

“The profession is at a pivotal moment that requires deliberate, nurse-led action to protect patient safety, ensure nurse well-being, and sustain public trust,” said Brad Goettl, DNP, DHA, APRN, FAAN, FACHE, Chief Nursing Officer of the American Nurses Enterprise, in a May 5, 2026 statement about the consensus findings from the Think Tank. (Download the consensus summary here: Artificial Intelligence in Nursing Practice: Consensus Findings from the ANA AI in Nursing Practice Think Tank.)

The Irreplaceable Human Element

Nurses remain the clinicians who are consistently involved in direct human-centered care. And with over four million registered nurses in the United States, nursing comprises the largest segment of the U.S. health care workforce.

No health profession is better positioned than nursing to evaluate how AI affects patient care. Every day, nurses integrate patient assessments, laboratory results, family concerns, symptoms, environmental factors, and clinical expertise to make decisions that affect lives. As AI becomes more sophisticated, that expertise becomes more valuable, not less.

Dr. Wieben’s work highlights an important distinction that often gets lost in day-to-day discussions about AI: that clinical judgment is something that AI cannot replace. In a 2025 article she published in The Online Journal of Issues in Nursing titled, “Overview and Summary: Artificial Intelligence in Nursing and Healthcare”, Dr. Wieben acknowledged the promise of AI while warning against applications that attempt to replace the uniquely human aspects of nursing care.

Discussing the rise of so-called “AI nurses,” she pointed out that nurses conducting patient assessments do much more than gather information. “A patient may say, ‘I’m fine,’ but it is clear to a nurse when they are not,” she wrote. “This is incredibly nuanced care that the nursing profession should not be delegating to AI.”

While Dr. Wieben acknowledges AI’s potential to improve efficiency and support decision-making, she argues that it cannot replace the therapeutic relationships, ethical reasoning, empathy, and clinical judgment that define nursing practice. Nurses remain uniquely positioned to evaluate how AI affects patients, clinicians, and the broader health care system.

And in an August 2026 publication titled, “When nurses are held accountable for AI-driven clinical decisions, they must be engaged early and meaningfully,” Dr. Wieben and members of the American Medical Informatics Association Nursing Informatics Workgroup assert that as AI becomes more embedded in health care, nurses may be increasingly held accountable for decisions influenced by AI tools, making it essential that they have meaningful roles in AI governance, implementation, education, and policymaking.

Leading, Not Reacting

Meaningful participation in AI development and implementation requires a clear understanding of how the technology works, and where it may affect patient care. To support that, Dr. Wieben created AI4Nurses, a website that provides research articles, policy reports, educational opportunities, podcasts, webinars, and other resources focused on AI in health care. She is also leading work as part of her leadership position with the Nursing Knowledge Big Data Science Initiative to develop microlearnings about AI for direct-care nurses.

Her national leadership has also continued to grow. Most recently, Dr. Wieben was appointed as the United States Country Co-Director for the AI Nurses Network, which is a “clinical research network designed to support and advance the role of nurses in the field of [AI]. It comprises an interdisciplinary team of nurses and AI experts, with a core team based in the United Kingdom and a network of country directors worldwide along with an independent advisory board that offers ongoing advice and support.” As the network expands on a global scale, the appointment of directors and co-directors like Dr. Wieben will help ensure that nurses across the world have access to various resources, news, and events related to AI in nursing and health care.

In addition, she was selected in 2024 to the Alliance for Nursing Informatics Emerging Leaders Program, a prestigious initiative designed to prepare nurse informaticists for leadership roles in policy, research, strategic planning, and innovation. She has also presented nationally on strategies for engaging frontline nurses in AI research and recently contributed to the Coalition for Health AI’s Ambient AI Implementation Toolkit.

In Madison, she is currently co-leading a study with partners at UW Health that is evaluating an ambient listening AI tool for inpatient nurses to understand impacts on time spent documenting and nursing professional well-being. The project is among the first large-scale evaluations of ambient AI specifically designed for inpatient nursing workflows, an area where documentation can consume up to 40% of a nurse’s shift.

Altogether, Dr. Wieben’s work, research, and leadership are helping prepare the profession for a future in which nurses are not merely users of AI, but architects and designers of its development, and advocates of its responsible integration.

Nursing’s Time to RISE

The conversations Dr. Wieben and her colleagues are leading do not exist in isolation. They are unfolding at a moment when UW–Madison itself is making significant investments in AI across disciplines.

Through the Wisconsin Research, Innovation and Scholarly Excellence initiative, better known as RISE, the university is expanding faculty expertise and interdisciplinary collaboration to accelerate AI research and innovation across campus. For nursing, that investment presents a unique opportunity not only to participate in AI’s future, but to help shape it.

Over the next three-to-five years, RISE-AI will add 88 faculty positions across disciplines and more than double campus investments in AI and related fields. New faculty will join schools, colleges, institutes, and centers across campus, accelerating work that spans health care, agriculture, materials science, communications, law, and the humanities.

Importantly, UW–Madison’s AI strategy recognizes that technological advancements must be coupled with human-centered inquiry. Through initiatives such as the RISE-AI Collaboration Headquarters and the Center for Humanistic Inquiry into AI and Uncertainty, researchers from all corners of campus are exploring how AI can be developed responsibly and ethically.

Out of the Shadow

As UW–Madison expands its AI ecosystem, the School of Nursing is advocating for nurses to be not merely participants in those conversations, but leaders within them.

On September 29, 2026, the School hosted its annual Littlefield Leadership Lecture. This year’s lecture, “Out of the Shadow: Why Nursing Must Lead AI, Not Follow,” featured nationally recognized nurse leader Dr. Roy L. Simpson, DNP, RN, DPNAP, FAAN, FACMI, assistant dean for data science education, professor, and co-director for the Center for Data Science at Emory University’s Nell Hodgson Woodruff School of Nursing. Through his work, Dr. Simpson asserts that as AI rapidly reshapes health care, nurses must help determine how those changes unfold.

During his lecture, he challenged nurses to embrace leadership roles in the design, implementation, and governance of AI systems, while trusting their expertise and prioritizing patient needs and their commitment to patient-centered care.

Although AI can enhance efficiency and support clinical decision-making, he emphasized that it cannot replace professional nursing judgment. “The algorithm doesn’t decide what’s true for the patient. The patient does,” he said. “Our job is to meet the patient there at the point of care, with the tools and the knowledge to get them to the best health possible.”

Dr. Simpson’s lecture stressed that nursing expertise must be visible in the systems that increasingly guide care. He urged nurses to document their clinical judgments, participate in technology governance, and insist that knowledge specific to nursing is incorporated into the data that drives AI responses in health care. “If nursing wasn’t in the training data, nursing isn’t in the answer,” he said.

Most importantly, he reminded attendees that influence requires more than representation. “A seat is not a voice,” he said, encouraging nurses to actively participate in conversations wherever technology decisions are being made. (To view the full lecture, visit: go.wisc.edu/w79sm5)

Building the Future with Nursing

The message emerging from the work of Drs. Wieben and Simpson is clear: nursing expertise belongs at every stage of AI’s development, implementation, and governance. It reflects a broader shift taking place across the profession. As AI becomes increasingly integrated into health care, nurses bring a lived perspective that no technology alone can provide—one grounded in clinical judgment, patient-centered care, and service to communities.

At the UW–Madison School of Nursing, that work is already underway. Through research, scholarship, education, innovation, and leadership, School faculty, alumni, staff, and students are helping shape how AI is developed, evaluated, and implemented in health care. Badger nurses have improved patient care, advanced science, strengthened communities, and changed lives for more than a century. As this technology continues to evolve, they are helping ensure its future remains guided by the expertise and human-centered values that have long defined the profession. The future of AI in health care should not be built around nursing; it should be built with nursing.