By: Sarah K. Wells MSN RN CEN CNL

I write a lot about technology, nursing, and healthcare. For the last year, I have been learning more and more about artificial intelligence (AI) in healthcare. AI has entered the clinical environment fast. As a nurse, here are tips how to work with it confidently and critically.

Healthcare has seen waves of technology come and go: electronic health records (EHR), telehealth, wearable monitors, virtual nursing are just a few examples. AI feels different, and for good reason. Unlike a new charting system or a “smarter” infusion pump, AI doesn't just assist with a task. It increasingly participates in clinical decision-making. In some cases, it can feel like it is making decisions for you. For nurses and other clinicians, that changes everything.

As a nurse in the AI era, you don't need to become a data scientist. But you do need to understand what AI is doing in your workplace, where it can go wrong, and how your professional judgment remains irreplaceable.

AI in Healthcare Is Already Here

Many nurses are already working alongside AI without realizing it.

Common applications include:

Clinical decision support. Algorithms embedded in EHR systems flag deteriorating patients, suggest sepsis alerts, or highlight potential drug interactions. If your hospital uses an early warning score that updates automatically, that's AI (or at least machine learning) at work.

Diagnostic imaging. Radiology and pathology departments increasingly use AI to complete a first-pass scan on images for anomalies, detecting potential fractures, tumors, or diabetic retinopathy, before a physician reviews them.

Predictive analytics. Some systems forecast which patients are at risk of readmission, pressure injuries, or falls, so care teams can intervene earlier. Predictive analytics are also being used in staff scheduling tools.

Administrative automation. AI is being used to transcribe clinical notes, pre-authorize insurance requests, and draft discharge summaries to potentially free up time that might otherwise be spent on paperwork.

What AI Does Well (and What It Doesn't)

AI excels at pattern recognition across enormous datasets. A model trained on millions of chest X-rays can identify subtle findings that a tired radiologist might miss at 2 a.m. In that narrow task, AI can be genuinely impressive.

But AI has real limitations that every nurse should understand:

It reflects the data it was trained on. If the training data underrepresented certain populations, which has historically been a common issue, the model may perform worse for those patients and worse, perpetuate bias that can increase instead of mitigate health inequities. Tools validated on predominantly white, male, or urban populations may not transfer well to your patient population.

It doesn't know what it doesn't know. AI systems can produce confident-looking outputs even when they're operating outside their reliable range. A model will rarely say "I'm not sure.” It will instead give an answer that it will make the user “happy”. AI is programmed to be agreeable, often prioritizing a user’s “happiness” over objective facts. Checking that answer against your clinical assessment is essential.

It can't account for context the way you can. The algorithm doesn't know that this patient is terrified of hospitals, that their family dynamics are complicated, or that they've been pushing through pain because they're worried about being a burden. You do. You are the essential NURSE in the AI loop.

It can hallucinate. AI language tools, used for note drafting or summarization, can generate plausible-sounding but factually incorrect content. Any AI-generated text that enters the clinical record needs human review and validation before it becomes part of the permanent chart.

Your Professional Accountability Doesn't Change

This is perhaps the most important thing to understand: when an AI tool makes a recommendation and you act on it, the professional and legal accountability still rests with you.

As of this writing, it is generally the rule that the clinician and their license take on the liability of any AI output (created thing) that is validated and acted upon. This may change in the future, but when you agree with clinical AI, you are signing off that you agree with it.

If a sepsis alert fires and you dismiss it without assessment or if you follow an AI recommendation that conflicts with your clinical judgment, you own that decision. "The algorithm told me to" is not a defense in a nursing board hearing or in a court of law.

This isn't meant to frighten you. It's meant to frame AI correctly: as a tool that informs your practice, not one that substitutes for it. The same way you wouldn't blindly follow an MD order without understanding it, you shouldn't blindly follow an AI recommendation without evaluating and validating it.

Practical rule: Do not have AI tell you what to do. Listen to its suggestions and then use critical thinking, clinical judgement, and verified resources to validate the guidance before acting on it.

Remember: Ultimately, it is your license that is on the line.

How to Think Critically About AI in Your Workplace

When a new AI tool is introduced to your unit, ask these questions:

What was it trained on? Has it been validated on data for patients or use cases like yours?
What does it optimize for? Some models optimize for cost reduction, not patient outcomes. Understand the goal and center discussions around patient care first and budget second
Who is accountable when it's wrong? Get clarity on your facility's policies before a problem arises. An ounce of prevention is worth a pound of response.
What does a false positive or false negative look like? Every screening tool makes errors. Know what they are, how to report them, and what to do when they come up.
How was staff trained? A good AI tool with poor implementation is a patient safety risk. Advocate for increased educational and training time to set up your patients, your team, and yourself for success.

You don't need to understand the math behind the model to ask these questions. Asking them is itself an act of clinical leadership.

AI and Nursing: A replacement or tool to leverage?

There is a version of the AI conversation that worries nurses about job replacement. That concern deserves honest engagement, not dismissal.

AI will likely automate parts of nursing work, particularly documentation, scheduling, and routine monitoring tasks. Whether that frees nurses to spend more time at the bedside or becomes a justification to reduce staffing is a policy and employer question, not necessarily a technology question. It will depend on how healthcare organizations choose to deploy the tools and adhere to their budgets.

What AI cannot replicate is the relational core of nursing: therapeutic presence, the ability to read a room, the skill of delivering hard news with compassion, the advocacy that happens when a nurse says "something isn't right with my patient." Those capacities are not pattern recognition on historical data. They are embodied, relational, and fundamentally human.

The nurses who will thrive in an AI-enabled environment are those who understand the tools well enough to use them critically, and who continue to develop the distinctly human competencies that no algorithm will replicate.

Practical Steps You Can Take Now

1. Learn your tools. Ask your nurse educator or informatics team to walk you through any AI-powered features in your EHR or monitoring systems. Understanding what they flag — and what they miss — makes you a safer practitioner.

2. Document your clinical reasoning. As AI tools generate more of the data in patient records, your documented reasoning becomes even more important as evidence that a human assessed the whole picture.

3. Speak up about concerns. If an AI alert seems to be firing inappropriately — either too often or not enough — report it. Your frontline observations are how these tools get improved or discontinued.

4. Engage with your professional and advocacy organizations. Groups like the American Nurses Association are developing position statements and guidelines on AI in nursing. Stay connected to those conversations.

5. Be a thoughtful voice in your unit. Not every colleague will have the same level of comfort or skepticism about AI. Being someone who asks good questions — without dismissing the technology or uncritically embracing it — is a form of leadership.

AI is not the future of nursing and healthcare, it is the present. Understanding it well enough to use it wisely, question it appropriately, and advocate for patients and clinicians in its presence, that is the new professional competency. And it is squarely within the tradition of what nurses have always done: meet the moment, with knowledge and compassion, on behalf of their patients, clinicians, and the community at large.

About the Author: Sarah K. Wells, MSN, RN, CEN, CNL is an experienced nurse career strategist dedicated to helping nurses and nurse practitioners of all experience levels and specialties achieve success in their nursing and NP journeys. Sarah founded New Thing Nurse and NTN Consults to help provide support and guidance to the nursing and healthcare community in a simple and direct format. Sarah’s vision is to foster a more supportive and fulfilled nursing world that spreads throughout healthcare and beyond.

Sarah has partnered with Ripple Health AI as a Nurse Advisor and Business Development Lead. The original version of this article appeared on the Ripple Health AI Insights blog.

Sarah is serving as a 2026 Advocacy Fellow with ANA-California, focusing on AI and equitable nurse staffing. Learn more about the 2026 ANA-California Advocacy Fellowships.

New Thing Nurse helps the nursing and NP community thrive in their careers! Join us on IG or Facebook @newthingnurse 🩺