\n From early warning systems to\n documentation assistants, artificial intelligence\n is gradually making its way into healthcare settings. For\n nurses, the challenge is not to delegate\n clinical judgment to an algorithm, but to understand\n what these tools actually measure, what they can improve, and under\n what conditions their use remains safe, explainable, and beneficial to the patient.\n
\nThe nursing profession relies on a combination of clinical, technical, interpersonal, and organizational skills. Monitoring health status, administering treatments, providing care, preventing complications, educating patients, and coordinating with other healthcare professionals are at the core of nursing practice. Electronic health records, monitors, and digital medical devices have been in use for several years, but they have not eliminated the need to observe patients, interpret readings in context, and exercise clinical judgment.
\n\n\n\nArtificial intelligence now adds a layer of analysis to this digital environment. In this article, the term refers in particular to models capable of identifying patterns in clinical data, generating a risk score, or—in the case of generative AI—generating text based on provided information. This distinction is important: a predictive system that calculates the risk of deterioration does not function like a generative assistant that summarizes a medical record, and their levels of evidence, risks, and uses are not the same.
\n\n\n\nOne of the best-documented examples involves the early detection of deterioration in hospitalized patients. In 2025, a multicenter pragmatic trial published in *Nature Medicine* evaluated CONCERN, an early warning system based on machine learning and patterns derived from nursing documentation. The study included 60,893 hospitalizations across 74 clinical units in two healthcare systems. The intervention group showed a 35.6% reduction in the instantaneous risk of death compared to usual care, expressed as an adjusted hazard ratio of 0.64. This result should not be interpreted as an absolute 35.6% reduction in mortality: it is a relative measure of instantaneous risk within the specific context of this trial.[1]
\n\n\n\nThe publication was subsequently corrected in April 2026. The authors revised the estimates regarding length of stay and 30-day readmission following a modeling error, without altering the statistical significance of the relevant results or the primary outcome regarding mortality. This correction illustrates a key rule for evaluating AI systems in healthcare: results should be interpreted based on their most recent version, along with their methodological limitations, and not as general evidence that a single tool will yield the same benefit in all hospitals.[2]
\n\n\n\nThis transformation is taking place at a time when nurses constitute the largest healthcare professional group worldwide. The World Health Organization’s report *State of the World’s Nursing 2025* estimates the global nursing workforce at 29.8 million in 2023, up from 27.9 million in 2018, while still estimating the global shortage at 5.8 million professionals. The same report identifies digital technologies as one of the areas for which the profession must be prepared.[3]
\n\n\n\nThese figures do not mean that AI is a direct solution to the staffing shortage. Technology can automate a task without resolving issues related to the field’s appeal, training, working conditions, or the geographic distribution of professionals. The challenge is therefore more specific: determining whether certain tools can improve the flow of information, risk detection, or documentation, without shifting the burden to new verification processes or compromising the patient-care relationship.
\n\n\n\nAI does not transform all nursing activities in the same way. Some applications rely on predictive models integrated into patient records, others on sensors and remote monitoring systems, while generative AI is primarily used for text generation or synthesis. To understand their impact, it is important to distinguish between what the system actually automates and what remains a clinical interpretation.
\n\n\n\n\n\nModels can combine multiple signals—such as vital signs, laboratory results, or documentation—to generate a score or an alert. The potential benefit lies in identifying trends that go beyond simply exceeding a single threshold. The CONCERN example shows that such a system can be evaluated in a real-world trial, but its results cannot be generalized to all early-warning systems.[1]
\nA risk score can help a team identify patients who require prompt reevaluation. However, it does not constitute a clinical decision. The healthcare professional must assess the patient’s condition, look for other signs, and interpret the result in light of the clinical context.
\nLarge language models can be used to structure, rephrase, or summarize notes. A systematic review published in 2025 on the uses of LLMs in nursing identified applications in decision support, patient education, documentation generation, and workflow optimization, while highlighting risks related to confidentiality, misleading outputs, and ethical issues. The literature remains inconsistent and does not support considering generated documentation to be reliable without human validation.[4]
\nConnected devices can transmit measurements collected outside the hospital. Algorithms can then flag certain changes. Here again, the clinical value depends on the quality of the sensors, the population being monitored, the frequency of false alarms, and the system in place to handle alerts.
\nGenerative AI assistants can help retrieve information or generate a summary based on a file. The WHO notes, however, that large multimodal models can produce statements that are false, inaccurate, biased, or incomplete. Their integration into healthcare therefore requires validation procedures appropriate to the critical nature of the task.[5]
\nThe real novelty, therefore, is not that nurses use digital tools. It lies in the fact that certain systems now generate scores, predictions, or content that can influence how information is interpreted and prioritized. This algorithmic mediation creates an additional need for verification, explainability, and traceability.
\n\n\n\nThe integration of AI does not turn nurses into algorithm specialists. Instead, it reinforces several aspects that are already part of their profession: observing, interpreting, coordinating, alerting, and explaining. In a more data-rich environment, professional value shifts less toward the mechanical collection of information and more toward putting that information into context.
\n\n\n\n\n\nA risk score may indicate that a patient warrants closer attention, but it does not necessarily describe the cause of the patient’s condition. The nurse must compare the system’s output with the clinical examination, reported symptoms, treatments, and medical history.
\nA statistically valid tool can become counterproductive if it generates too many alerts or if it does not integrate well with the department’s practices. The professional then helps identify situations in which an alert is useful, redundant, or misleading.
\nWhen a generative assistant prepares a handover or a summary, the nurse remains responsible for verifying that all essential elements are included, that nothing has been made up, and that the wording does not present a hypothesis as an established fact.
\nTheir close contact with patients gives nurses practical knowledge that is difficult to replace with a purely technical assessment. Their involvement in the design, testing, and auditing of systems is particularly important when the tool alters monitoring or documentation practices. Recent studies on ambient recording devices in nursing specifically highlight the risks of misinterpretation, omission, and bias when nurses are not sufficiently involved in the development and oversight of these systems.[6]
\nA system can process data, but it cannot physically be present, listen, or explain things to the patient. The more tools automate certain information-related tasks, the more important it becomes to ensure that there is sufficient time and the right conditions for human interaction.
\n\n The nurse’s new role is therefore not merely that of an operator of intelligent systems. It involves integrating these tools into clinical practice, where decisions remain context-specific, traceable, and commensurate with the level of risk.\n
\nThe fundamentals of the profession remain a priority: clinical observation, pharmacology, monitoring, prevention, communication, coordination, and clinical reasoning. AI does not diminish the importance of these skills; rather, it creates new situations in which they must be applied.
\n\n\n\n\n\nThe answer depends on what we mean by “reliable.” A system can improve the detection of an event in a given context without improving all aspects of the quality of care. Reliability must therefore be examined from several perspectives: measurement accuracy, predictive performance, quality of integration into the department, and the teams’ ability to act appropriately on the information generated.
\n\n\n\nThe CONCERN trial provides a stronger level of evidence than a simple technical demonstration, as it was conducted in real-world clinical settings and compared teams guided by the system to usual care. The adjusted results published in 2026 still show a statistically significant reduction in the instantaneous risk of death, a more modest reduction in length of stay, and a decrease in the instantaneous risk of sepsis. The study also observed an increase in the instantaneous risk of unanticipated transfer to the intensive care unit, a finding that may be consistent with earlier detection but must be interpreted within the context of the protocol.[1][2]
\n\n\n\nThese results do not demonstrate that a standalone algorithm “saves lives.” The intervention involves a system integrated into patient records and team workflows, with an organizational chain of command for handling alerts. The observed benefit therefore depends on the combination of technology and organizational structure, and not solely on the model itself.
\n\n\n\nAI can thus improve certain monitoring and decision-support processes when it is properly evaluated, integrated, and supervised. It does not make care infallible. The relevant question is not simply “Is the model effective?” but “In what situations, for which patients, with what data, and within what organizational framework does this effectiveness actually improve care?”
\n\n\n\nIt would be premature to describe a single model of “the nurse of the future.” Practices vary depending on departments, countries, digital infrastructure, and regulatory frameworks. Nevertheless, based on the tools currently being evaluated, several possible paths are plausible.
\n\n\n\nWe will also need to consider a less obvious effect: the impact of automation on learning the profession. As certain tasks—such as data collection, writing, or sorting—are gradually taken over by systems, organizations will need to ensure that professionals in training continue to acquire the skills necessary to understand the source of information and detect inconsistencies.
\n\n\n\nThe future of the profession will therefore depend less on a scenario of replacement than on how healthcare facilities allocate responsibilities between professionals and systems. The more a task directly affects patient safety, the stricter the requirements for assessment, traceability, and supervision must be.
\n\n\n\nArtificial intelligence can analyze signals, generate a score, or prepare a summary. It does not share the nurse’s professional responsibility and does not experience the situation the patient is going through. This difference is not a mere philosophical detail: it determines how these systems should be integrated into patient care.
\n\n\n\nA useful tool is one with a clear purpose, whose performance is evaluated in a relevant context, whose errors can be identified, and whose use does not compromise clinical vigilance or patient autonomy. For several years, the WHO has emphasized the need to place ethics, human rights, transparency, and accountability at the heart of AI governance in healthcare.[9]
\n\n\n\nFor nurses, the most significant transformation may therefore be less dramatic than the complete automation of care. It lies in the ability to work with more information while maintaining a clear hierarchy between data, alerts, interpretation, and decision-making. The system can flag issues. The professional must still observe, contextualize, explain, and take action.
\n\n\n\nTechnology can make certain information visible sooner. It can also create new blind spots. The challenge of augmented care, therefore, is not to choose between artificial intelligence and nursing expertise, but to organize their integration in such a way that technology truly enhances the safety, continuity, and quality of the care relationship.
\n\n\n\nThe question now is this: Will healthcare facilities use AI primarily to streamline workflows, or to give healthcare professionals the conditions they need to provide more attentive monitoring and be more available to patients?
\n\n\n\n\n To further explore these topics—\n AI in healthcare, human oversight, and the evolution of skills—\n here are four additional posts from the aivancity blog.\n
\n\n [1]\n Rossetti, S. C., Dykes, P. C., Knaplund, C., et al. (2025).\n Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.\n Nature Medicine, 31, 1895–1902.\n \n View the study\n \n
\n\n\n [2]\n Rossetti, S. C., Dykes, P. C., Knaplund, C., et al. (2026).\n Author Correction: Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.\n Nature Medicine, 32, 1556.\n \n View the correction\n \n
\n\n\n [3]\n World Health Organization. (2025).\n State of the World’s Nursing Report 2025.\n \n View the report\n \n
\n\n\n [4]\n Song, J., Liu, W., Wang, Y., Hu, X., Chen, L., Wu, X., Zheng, C., & Gu, Q. (2025).\n Application and Challenges of Large Language Models in Clinical Nursing: A Systematic Review.\n Computers, Informatics, Nursing, 43(9), e01328. \n \n View the study\n \n
\n\n\n [5]\n World Health Organization. (2025).\n Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models.\n \n View the report\n \n
\n\n\n [6]\n Topaz, M. (2025).\n Invisible Scribes: Can Nurses Trust Ambient AI for Clinical Documentation?\n Journal of Continuing Education in Nursing, 56(9), 358-359.\n \n View article\n \n
\n\n\n [7]\n European Commission. (2026).\n Navigating the AI Act.\n \n Visit the European Commission\n \n • \n European Union.\n Regulation (EU) 2024/1689, Article 6. \n \n View the regulation\n \n
\n\n\n [8]\n Bracken, A., Reilly, C., Feeley, A., Sheehan, E., Merghani, K., & Feeley, I. (2025).\n Artificial Intelligence (AI)-Powered Documentation Systems in Healthcare: A Systematic Review.\n Journal of Medical Systems, 49(1), 28.\n \n View the study\n \n
\n\n\n [9]\n World Health Organization. (2021).\n Ethics and Governance of Artificial Intelligence for Health.\n \n View the report\n \n
\n\nFrom early-warning systems to documentation assistants, artificial intelligence is gradually making its way into healthcare settings. For nurses, the challenge is not to delegate clinical judgment to an algorithm, but to understand this…
\n","protected":false},"author":7,"featured_media":658576,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[61],"tags":[],"class_list":["post-658568","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ia-metiers"],"yoast_head":"\n