The use of artificial intelligence (AI) in nursing practice is no longer an abstract concept. It is already a part of nursing, and its role is likely to continue growing.
The use of AI in hospitals reflects this broader shift. In 2024, 71% of hospitals reported integrating predictive AI into their electronic health record (EHR) systems to forecast outcomes and identify risks — up from 66% in 2023 — according to the Assistant Secretary for Technology Policy/Office of the National Coordinator for Health Information Technology.
If you’re considering pursuing a nursing education to start or advance in your career, it’s vital to understand how AI tools can inform patient care, where they fit into hospital workflows, and why human judgment still matters.
How’s AI Being Used in Hospitals?
AI adoption has spread across the entire healthcare sector. Pharmaceutical companies use it to support drug development. Physicians’ offices employ AI to streamline administrative functions such as scheduling and documentation. Meanwhile, hospitals have begun to leverage AI for a range of clinical and operational applications.
Below are among the most notable examples of the ways hospitals are using AI.
Clinical Decision-Making
One of AI’s most promising uses in hospitals is in clinical decision support. AI systems can analyze massive amounts of patient data, such as medical histories, lab results, and diagnostic images, and provide insights that inform care. For example, AI-based decision tools can outperform older, more traditional methods of evaluating patients for clinical deterioration.
This capability is especially valuable in dynamic environments where rapid response is critical, such as hospital emergency departments. If you’re an aspiring emergency room nurse, AI can serve as another layer of support, helping you recognize changes in your patients sooner and respond more quickly.
Diagnosis
Hospitals also use AI to support diagnostics, especially in imaging. AI-assisted imaging can help clinicians detect lung nodules on CT scans, aid in the interpretation of mammograms, and support other specialized applications.
This shift can greatly impact the care environment you may enter, as nurses are often part of the team translating AI insights into timely patient care.
Workflow Optimization
The application of AI in hospitals isn’t limited to bedside care; organizations are also using it to improve operations. Some of the fastest-growing use cases for predictive AI include billing and scheduling. AI can also help hospitals manage inventory and bed capacity. These improvements can ease some of the strain on nurses, particularly in environments with ongoing workforce shortages, and reduce the risk of burnout.
Documentation, scheduling, and patient flow all influence the pace of a shift. When those systems work better, you may be able to devote more time to direct patient care.
How Do Nurses Interact With AI?
In practice, nurses may use AI in both clinical and administrative capacities. Some systems work in the background, while others provide more visible support at the point of care. As AI adoption expands, you’re increasingly likely to work with these systems rather than around them.
Examples of how nurses may encounter AI in hospital environments include:
Clinical decision support tools that analyze EHR data, lab results, and patient monitoring information to highlight possible complications or support care planning
Medication safety systems that cross-check prescriptions against patient data to catch potentially dangerous interactions or dosage concerns
Documentation tools that use AI’s natural language processing capabilities to extract useful information from nursing notes and reduce the burden of charting
Scheduling and staffing systems that forecast patient volume and help allocate resources more efficiently
Patient education applications such as AI-powered chatbots that can provide medication reminders and discharge information
However, even with these tools, a nurse’s role remains deeply human. AI can provide meaningful insights and even enhance care in many cases, but it can’t replace bedside assessment, communication, empathy, or professional judgment.
How Can Nursing Programs Prepare Nurses for the Future of Healthcare?
As AI becomes more common in clinical settings, nursing programs have an opportunity to help students build the knowledge and judgment to use it thoughtfully. Although nursing students generally have positive attitudes toward AI, they still need guidance on how to use it effectively and ethically to support sound clinical decision-making.
Build AI and Digital Literacy
Practical nursing (PN) and Associate Degree in Nursing (ADN) programs can help prepare students for the future by providing a foundation in AI and digital literacy. Nurses don’t need to be programmers, but they do need to understand the role that AI plays in clinical workflows and the fundamentals of how to apply AI in their practice.
Through hands-on exposure, such as simulations and guided classroom activities, PN and ADN nursing students can begin to understand how AI systems may influence various aspects of their job. If you engage with these technologies for the first time only after beginning your practice, the learning curve may feel steeper.
Hone Ethical Judgment
Nursing programs can also help students think through the ethical implications of using AI. The increasing prevalence of AI systems in hospitals and other clinical settings will require nurses to consider ethical issues such as privacy, bias, and transparency.
That ethical grounding is essential, allowing nurses to use AI to supplement patient care without becoming overreliant on it. As a new nurse, you’ll need to balance technological insight with compassion and context.
Develop Critical Evaluation Skills
Nursing education should also help students learn how to assess AI outputs thoughtfully. Future nurses will need to be able to interpret AI insights through the lens of their own clinical judgment, including recognizing when something seems off.
If an AI-generated recommendation doesn’t align with what you observe in a patient, you’ll need the confidence to question it. The ability to trust your judgment and draw on your expertise will be crucial in a care environment where technology can inform — but not make — decisions for you.
What AI Means for the Future of Nursing
As the use of AI in hospitals continues to expand, nurses will need both technical awareness and strong clinical judgment to work with these technologies effectively. With the right preparation, you can enter the field ready to use these systems thoughtfully, without losing sight of nursing’s human side.
If you are considering becoming a nurse, explore the nursing programs at Fortis. Our PN and ADN programs can help you build the foundational clinical skills and technological competence to deliver patient-centered care in an evolving healthcare landscape.
Learn how Fortis can prepare you for the future of nursing.
Recommended Readings
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LPN vs. RN: What’s the Difference?
Sources:
American Hospital Association, How AI Is Improving Diagnostics, Decision-Making, and Care
Assistant Secretary for Technology Policy/Office of the National Coordinator for Health Information Technology, Hospital Trends in the Use, Evaluation, and Governance of Predictive AI, 2023-2024
Bioengineering, “The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century”
International Journal of Environmental Research and Public Health, “Values Clarification Exercises to Prepare Nursing Students for Artificial Intelligence Integration”
JMIR Nursing, “Readiness and Acceptance of Nursing Students Regarding AI-Based Health Care Technology on the Training of Nursing Skills in Saudi Arabia: Cross-Sectional Study”
Nurse Education in Practice, “Artificial Intelligence (AI) Applications in Healthcare and Considerations for Nursing Education”
Nursing Outlook, “N.U.R.S.E.S. Embracing Artificial Intelligence: A Guide to Artificial Intelligence Literacy for the Nursing Profession”
The Online Journal of Issues in Nursing, “Artificial Intelligence in Nursing Practice: Decisional Support, Clinical Integration, and Future Directions”