How Artificial Intelligence (AI) is Used in Healthcare

Skeptical of AI? Good.

If you also realize that artificial intelligence (AI) in healthcare is the present, not just the future, but don’t fully understand its impact, you’re in the right place. This guide shows where it actually works, legitimate concerns, and paths to adopting it safely.

According to a recent industry report, the global market size of AI in healthcare reached $26.57 billion in 2024 and is expected to grow by 38.62 percent from 2025 to 2030.

The healthcare field needs to continuously improve, and AI is here to revolutionize, streamline, and redefine patient care, operational workflows, and workforce management.

The healthcare industry is at a crucial inflection point, where embracing AI technology is a necessity rather than an option since it improves services, obtains better patient outcomes, and lightens the load on clinicians’ and administrators’ shoulders.

From diagnostics and imaging to administration and staffing, AI in healthcare applications can make medicine smarter, safer, and more sustainable.

The question you should be asking isn’t “will AI change healthcare?” It’s “how can I use it to improve outcomes, protect privacy, and support my teams?”

What is AI in healthcare?

Artificial intelligence in healthcare refers to computer systems designed to perform complex tasks for medical purposes that would otherwise require human intelligence. This can include making informed inferences from medical images or large clinical datasets, interpreting natural language, and supporting clinical decisions.

At its core, AI in medicine uses machine learning (ML), deep learning (DL), and natural language processing (NLP) algorithms to find patterns, make predictions, and automate routine tasks. This technology is becoming a true co‑pilot for clinicians, assisting with both clinical and administrative functions.

AI is the engine, and clinicians hold the wheel.

While machine learning identifies subtle patterns in data and predicts patient risks, NLP translates unstructured notes and medical literature into actionable insights. AI in healthcare administration also addresses billing, scheduling efficiency, and real-time calculations on staffing requirements.

Types of AI being applied in healthcare

It is critical for organizations aiming to modernize their care models to know what the different types of AI used in healthcare are.

Here’s what is in healthcare’s new toolkit: algorithms that learn, see, and predict.

Machine learning

Machine learning is the backbone of most AI healthcare applications. These algorithms learn from large volumes of structured data (like imaging or lab values).

For example, ML is currently used in early cancer detection, often outperforming traditional approaches.

Natural language processing

Natural language processing interprets clinical narrative and turns it into computable data. It is used for summarizing physician notes, extracting critical details from electronic health records, and supporting medical coding and compliance.

Predictive analytics

AI-driven predictive analytics uses historical data to forecast patient outcomes, demand surges, and potential clinical deterioration.

Computer vision

AI-powered computer vision supports radiologists and pathologists by quickly and accurately reading medical scans, often spotting abnormalities that human eyes might miss.

For example, AI tools now detect lung nodules and breast lesions with higher sensitivity, resulting in improved care outcomes for patients.

Robotics and automation

AI-driven robots assist in surgery, logistics, and even patient rehabilitation. These technologies automate repetitive and labor-intensive processes, from medication delivery to surgical navigation.

Generative AI

Generative AI is the newest entrant, producing synthetic images for training, generating documentation, and acting as a virtual assistant for staff and patients alike.

How the healthcare industry stands to benefit from AI

So, what are the benefits of AI being used in healthcare? Here are a few examples:

Reduced clinician workload: Automated documentation, scheduling, and digital assistants save time, allowing clinicians to focus on direct patient care and reducing burnout.
Operational efficiency: Predictive supply and demand tools optimize inventory, bed management, and shift allocation.
Cost savings: By preventing unnecessary tests, minimizing manual errors, and improving scheduling, AI technologies help organizations control costs and do more with less.
Clinical decision support: AI supports physicians by synthesizing literature, surfacing red flags, and contributing to faster, evidence-based decisions.
Improved patient outcomes: From chronic disease management to early detection of cardiac events, AI provides proactive support that improves quality of life and reduces hospital readmissions.

AI means less paperwork and more time with patients.

Applications of AI in healthcare

Discover some of the top use cases and ways that AI is being applied in healthcare.

1. AI-powered medical imaging and diagnostics

ML algorithms analyze complex medical images such as X-rays, MRIs, and CT scans to detect abnormalities more accurately and faster than human radiologists. This enables earlier diagnosis of diseases like cancer, improving treatment success and patient outcomes.

2. Personalized medicine

AI analyzes genetic information, lifestyle data, and clinical history to tailor treatment plans to individual patients. By focusing on the most effective interventions for each patient, AI reduces trial-and-error in therapy and improves efficacy.

3. Remote patient monitoring

AI processes continuous data from wearable devices or home sensors to detect early signs of deterioration in patients with chronic conditions. This data allows prompt clinical intervention, reducing hospitalizations and costs.

4. Clinical documentation automation

Natural language processing tools summarize physician notes, extract relevant information, and automate coding, reducing clinician administrative workload and documentation errors.

5. Virtual health assistants and chatbots

AI-driven assistants help patients schedule appointments, answer health questions, and triage symptoms outside regular clinic hours, increasing patient engagement and access.

6. Predictive analytics for patient risk

By analyzing historical health data, AI models predict risks for readmission, complications, or disease progression. Healthcare teams use these insights to target preventive care and optimize resource allocation.

7. Drug discovery and development

AI speeds up the drug discovery process by simulating molecular interactions, predicting compound efficacy, and identifying candidate molecules, cutting down development time and costs.

8. Robotic-assisted surgery

Robotic systems guided by AI enable precise, minimally invasive surgeries with improved accuracy, reduced recovery times, and fewer complications.

9. Hospital operations optimization

AI optimizes bed management, staff scheduling, and supply chain logistics by forecasting demand and resource needs, improving efficiency and patient flow.

10. Population health management

AI aggregates and analyzes community health data to predict disease outbreaks, monitor vaccination campaigns, and inform public health interventions, enhancing preventative care. AI in healthcare administration and staffing continues to prove particularly effective, helping facilities be ready for future workforce demands and better face the nurse shortage.

Potential risks of AI in healthcare

Despite tremendous progress, organizations must navigate the potential risks associated with the following ethical concerns of AI use in healthcare:

AI bias in healthcare: Algorithms trained on incomplete or biased datasets can perpetuate disparities, underscoring the need for representative data and continual model auditing.
Transparency and explainability: Complex models, especially deep learning systems, can be “black boxes,” making their logic difficult for clinicians to interpret.
Safety and regulation in medicine: Regulatory frameworks are still evolving; oversight is critical to ensure AI recommendations align with best practices and patient safety.
Privacy and cybersecurity: The vast amount of data required for AI also raises new privacy and security concerns, as breaches or misuse could have severe impacts.
Overreliance on automation: While AI can serve as a potent co‑pilot for clinicians, it must never replace human judgment entirely. It is crucial to keep clinicians “in the loop” so they can override or question AI outputs when appropriate.

How the healthcare industry can adapt to AI

Facilities, administrators, and clinicians can take practical steps to benefit from AI while maintaining safety and trust.

Be curious

Healthcare teams need to stay open to learning about new AI solutions. Instead of fearing AI as a vague threat, curious teams should ask the following questions: What problem does this solve? How can it fit into our workflow? What risks should we watch for?

By staying open to possibilities, leaders and staff can spot use cases beyond the obvious—whether that’s AI for diagnostics, staffing forecasts, or even streamlining documentation.

A culture of curiosity also encourages experimentation and continuous learning. Facilities that embrace “let’s test it” rather than “we must get it perfect” adapt more quickly and get more out of the technology.

Related: Test out your curiosity by exploring Nursa’s AI chatbot for nurses

Conduct small pilot projects

Start with one specific problem area, such as reducing documentation burden, improving scheduling, or solving staffing inefficiencies.

Run a targeted AI pilot and measure results before scaling up—a method supported by evidence in case studies across top health systems.

Train and upskill staff

Even the most sophisticated AI tool will fail without staff buy-in. Regular training sessions ensure that clinicians, schedulers, and administrators understand how to use the technology, what its limitations are, and how it can make their work easier.

Equip your team to understand, evaluate, and interact with AI-powered tools, both to boost confidence and ensure correct use.

Fit AI into existing workflows

systems that integrate seamlessly with electronic health records (EHRs), staffing platforms, and scheduling systems makes adoption far smoother. AI systems should support existing processes to drive real benefits, not disrupt them.

The best solutions operate in the background—automating tasks, surfacing insights, and removing friction—so clinicians and administrators can spend more time focusing on patients and less time fighting with technology.

Monitor, measure, and adapt

AI is not “set it and forget it.” Continuous governance is essential for safety, compliance, and effectiveness. Facilities should track KPIs such as documentation time saved, reduced scheduling errors, cost savings, and, most importantly, patient outcomes.

Gathering staff feedback alongside the numbers helps identify hidden issues and opportunities. Facilities that treat AI as a living system—one that requires adjustment, retraining, and refinement—will see the most sustainable benefits.

Reducing nurse turnover with AI support

Nurse turnover is a pressing issue for the healthcare sector, where administrative overload, emotional fatigue, and inflexible schedules contribute to significant turnover rates. According to an article published by Becker’s Hospital Review, the average registered nurse turnover rate in the healthcare sector is over 16.4 percent, costing facilities thousands per departure.

AI in healthcare staffing now tackles these pain points directly. With dynamic, AI-powered scheduling platforms that analyze staffing levels, patient acuity, and even staff wellbeing indicators, facilities can predict potential burnout and redistribute shifts accordingly.

Integration of artificial intelligence creates responsive staffing solutions that balance workload, give nurses more input over their shifts, and help administrators anticipate and intervene before turnover escalates.

AI systems now generate optimized schedules and surface at-risk employees—filling open shifts efficiently while respecting staff preferences.

Explore more resources on Nursa

The potential uses of AI in healthcare are limitless and have become a great asset for facilities, making administrative, clinical, and staff management processes more efficient and accurate. That said, clinicians are irreplaceable, and their judgment is crucial in every decision.

Explore other facility-relevant resources from Nursa to discover ways to make processes more efficient, find specific uses of AI, or develop strategies to implement flexible staffing solutions in your facility.

Sources:

AI in healthcare: Uses, benefits, and how to embrace change

Key takeaways:

What is AI in healthcare?