How to Use AI in Healthcare: Top Use Cases and Real-World Examples

• AI/ML
• July 29, 2025

by Kumarpal Nagar

AI is transforming healthcare by enabling systems to analyze, predict, and respond more intelligently. From improving diagnostics and outcomes to enhancing patient experiences and accelerating drug discovery, AI use cases in healthcare practices are already in action. Leading hospitals, such as the Mayo Clinic, Johns Hopkins, and Cleveland Clinic, are putting these use cases into practice. Read the blog to explore how AI is being applied in real healthcare settings with real-world examples.

Whether you’re a healthcare provider or tech enthusiast curious about AI use cases in healthcare, you may have asked, “Is AI just hype, or can it actually improve patient care?”

The fact is, most of everything you hear about using AI in healthcare practices, this tech can do all. Be it improving patient experience or treating patients with precision, AI in healthcare use cases can help achieve all.

Hospitals like the Mayo Clinic and Johns Hopkins are using it to detect diseases earlier, personalize treatments, and reduce staff overload, setting strong examples of AI in healthcare applications.

By the end of this blog, you’ll know 9 of the best practical use cases of AI in healthcare with simple explanations covering how they emerged, what that use case is, how it works, and its benefits with real-world examples. So you can see which AI use case fits where in your healthcare software development strategy.

In short, this guide gives you clarity, proof, and direction, whether you’re evaluating AI tools, trying to streamline operations, or looking to offer better outcomes.

Top 9 AI Use Cases in Healthcare Practices

AI is transforming healthcare delivery by supporting clinical decisions, pinpointing opportunities through operational analytics, and enhancing workflows. Let’s explore the top 9 real-world use cases of AI for the healthcare industry, using which providers are seeing patient care excellence:

1. AI for Early Disease Detection in Medical Imaging

As per BMJ Quality & Safety research, every year, nearly 795,000 Americans either lose their lives or are permanently disabled due to misdiagnosis. In serious diseases, radiology accounts for the majority share in causing diagnostic errors. It happens because of the fatigue created by managing hundreds of scans conducted daily.

Hence, it asked for the emergence of AI use cases in disease detection, specifically through medical imaging data analysis.

The AI-powered imaging solution is built using machine learning models, especially deep learning algorithms, which are trained on thousands of annotated images to detect abnormalities, like cancer, pneumonia, strokes, hemorrhages, or fractures, that radiologists may miss. These models can flag anomalies in seconds, assisting doctors in diagnosing faster and with higher accuracy.

Because of this capability, this AI use case for disease detection can be majorly used in healthcare areas, like:

• Radiology for detecting disease signs from MRI, CT, and X-ray interpretation
• Oncology for tumor detection
• Neurology for stroke diagnosis
• Pulmonology for detecting pneumonia and flu signs (like COVID-19)

So, if you plan to leverage this AI use case in your healthcare operations, you can benefit from:

• Faster and more accurate diagnoses
• Reduced diagnostic errors and false negatives
• Enables early intervention, saving lives and cutting treatment costs

2. Predicting Patient Outcomes with AI Analytics

Centers for Medicare and Medicaid Services (CMS) data highlighted by AMCM reveal that each year, around 3.8 million patients across the U.S. region are readmitted within 30 days of discharge. This readmission cost hospitals somewhere around $52.4 billion. This happens due to poor patient care and can be mitigated if the right measures are enforced during discharge.

This serious concern for patient readmission within 30 days of discharge calls for the implementation of AI analytics solutions that help predict patient outcomes.

This AI-powered predictive data analytics solution uses data analysis, machine learning, artificial intelligence, and statistical models to assess patient data from connected EHR data, vitals, lab results, and even wearable device data. This analysis helps predictive models identify risks, such as chances of readmission or ICU transfer, mortality, or complications.

Many healthcare organizations prefer to use this intelligent predictive system in their areas, like:

• Emergency and Intensive Care Units (ICU)
• Cardiology to predict heart failure risk
• Post-operative recovery wards
• Chronic care programs

If leveraged correctly, this use case can benefit your organization in terms of:

• Providing proactive care over-reactive treatments
• Helping doctors personalize care plans through risk stratification
• Reducing preventable complications and hospital readmissions

3. Virtual Health Assistants for 24/7 Patient Support

Approximately half the world’s population lacks access to essential healthcare services. Moreover, there are many hospitals facing shortages of full-time equivalent (FTE) physicians. The count may reach 11 million globally by 2030 and 187,130 by 2037 in the US alone.

This shortfall in your hospital may lead to a poor patient experience if there is no one to answer patient queries in case they need quick resolutions to their health issues.

In that, investing in virtual health assistants (whether AI agent development or AI-powered chatbot assistants) can be instrumental. These AI agents/assistants are trained on vast datasets and use NLP to comprehend patient queries and respond to them. If integrated with EHR or hospital ERP or CRM systems, it can interact with patients conversationally, understand context, and provide accurate, evidence-based responses.

Hence, many hospitals prefer to implement virtual health assistants for:

• Primary care clinics and telemedicine platforms
• Mental health services (CBT-based chatbots)
• Chronic care management
• Outpatient follow-up services

You should also consider implementing virtual health assistants (whether AI agents or chatbots) to enable 24/7 patient support because it: 

• Reduces wait times and call center loads
• Reduces the burden on the front desk and nursing staff
• Improves patient engagement and satisfaction
• Offers accessible healthcare advice anytime, anywhere

4. Accelerating Drug Discovery with AI Models

It traditionally takes 10 to 15 years to bring a new drug to market because it involves stages, including basic research, preclinical development, clinical trial phases (from I to III), and finally, regulatory review and approval.

What if, say, there’s a way to minimize the drug discovery time?

Yes, that’s correct. If leveraged AI is used in drug discovery processes, it could potentially halve discovery timelines by 2030. Not just that, leveraging AI-enabled workflows could reduce the time and cost of bringing a new molecule to the preclinical candidate stage by up to 40%.

AI models shorten drug discovery timelines by involving big data analysis of genomic data, protein structures, and disease pathways; integrating predictive models to know how targets respond to intervention; involving virtual compound screening; and leveraging generative AI in healthcare data and drug molecular design and optimization. Moreover, AI can help to conduct preclinical studies (such as toxicity prediction, biomarker identification, etc.) and clinical trials (including optimizing trial design, patient recruitment, real-time monitoring, data analysis, and drug repurposing).

Many healthcare specialists and bio researchers are leveraging these AI models specialized for drug discovery in areas like:

• Oncology for drug target identification, lead compound optimization, and predicting drug efficacy and toxicity
• Rare disease control for accelerating the identification of potential drug candidates, optimizing clinical trial designs, and personalizing treatments
• Virology studies for identifying antiviral compounds faster, predicting viral protein structures, drug repurposing, broad-spectrum antivirals, and more.
• Neuroscience research by identifying novel drug candidates, repurposing existing compounds, and modeling disease progression for complex disorders like Alzheimer’s and Parkinson’s.

Many healthcare and bioresearch firms prefer to leverage AI models in drug discovery and clinical trials to reap benefits like:

• Cutting drug development costs and time
• Increase success rates by filtering poor candidates early
• Enable faster response to emerging diseases

5. Automating Healthcare Administrative Tasks

One study says that healthcare professionals spend 35% of their time, nearly 15 hours a week, on paperwork and administrative tasks, according to NIH. That’s a general overview. If we check physicians working in specialty care like rehabilitation, you may find them spending at least 19 hours per week on these tasks, according to the Medscape Physician Compensation Report.

All of these can lead to burnout and reduced patience, which can even delay patient care and leave the administrative staff frustrated.

That’s where AI-powered process automation can play an instrumental role in hospital administrative tasks, including data extraction, form filling, summarizing doctor-patient conversations, and other clinical documentation tasks.

You can leverage this AI-powered workflow automation use case in:

• Outpatient clinics and private practices with appointment scheduling, EHR documentation, patient intake & triage
• Hospital admin departments with staff scheduling, bed & resource management, supply chain automation
• Insurance and billing offices with claims processing, medical coding, and prior authorization
• Telehealth workflows like virtual check-ins & follow-ups, consent & documentation, and real-time transcription & summarization. 

If you use AI-powered automation strategically, you can benefit from:

• Reduction in administrative burden on staff
• Improvement in documentation accuracy and consistency
• Improvement in doctors’ job satisfaction in patient care
• Time-saving in healthcare administrative tasks

6. AI-Powered Claims Processing & Fraud Detection

Though healthcare insurance claims help patients to get funds for better care, some also take advantage of it through fraudulent activities. If we see the numbers, then in the U.S. only, healthcare claim fraud and abuse account for 3% to 10% of total healthcare expenditures, potentially leading to a loss of $300 billion per year. (Source: Milliman)

This is a serious issue that healthcare insurance-providing firms and governing bodies should deal with. That’s where AI-powered claims processing & fraud detection systems emerge as the problem-solving AI use case in healthcare.

AI revolutionizes insurance claims management by speeding up approvals and catching fraudulent claims before they appear. For it, it leverages predictive analytics in insurance management and claims processing automation.

Trained on a massive dataset of fraudulent behaviors and potential errors, an AI-powered claims processing and fraud detection system analyzes massive incoming processing data and flags fraudulent patterns in billing, diagnoses, and service claims. Moreover, it also automates legitimate claim approvals, which reduces processing time.

This use case is highly valuable for:

• Insurance companies do this by automating claims reviews, anomaly detection, speeding reimbursements, and using predictive analytics.
• Third-party billing services with intelligent claim scrubbing, real-time eligibility checks, fraud pattern recognition, and automated appeals handling.
• Hospital finance departments with revenue cycle optimization, fraud & waste monitoring, audit preparation, and denial prediction.
• Government healthcare programs (e.g., Medicare) with large-scale fraud surveillance, improper payment reduction, policy enforcement, and resource allocation.

Leveraging AI-powered claims management, these healthcare entities can benefit from:

• Faster reimbursement cycles
• Fewer claim denials and disputes
• Reduced financial risk

7. Personalized Treatment Plans through Precision Medicine

There are “one-size-fits-all” medicines for certain diseases and symptoms, but what about those meds’ suitability for all sorts of patients? It might work on the masses, but what about those few with special health conditions, allergies, and rare diseases?

Hence, there is a need for personalized treatment plans through precision medicines that are made for specific kinds.

It means leveraging AI to enable truly personalized care by analyzing a patient’s genetics, lifestyle, medical history, and current symptoms to tailor treatment options.

And how does AI do it? AI tools process genomic data and match it with clinical databases to suggest optimal therapies or predict drug responses. This is often integrated with pharmacogenomics and EHR systems.

Thanks to this nature, AI in precision medicine and personalized care plans can work best for:

• Clinical trials
• Oncology for targeted therapies and immunotherapies
• Cardiology for hypertension and cholesterol management
• Mental health to prepare genetic responses to antidepressants
• Rare and genetic disorders

When you leverage AI-powered personalized treatment plans and precision medication, you benefit from:

• Higher treatment efficacy with fewer side effects
• Reduction in trial-and-error in prescribing
• Support for better clinical outcomes and patient trust

8. Robotic Surgery & Assistance

Medical errors have been a serious problem in the public health category. Here, medical errors could be surgical, diagnostic, medication, equipment failure, or many. In this specific case, surgical error has the potential to take the life of a patient.

As per the report of the National Library of Medicine, in the U.S., around 400K hospitalized patients suffer from preventable harm, and more than 200K deaths occur due to preventable medical errors. These errors cost the healthcare system $20 billion each year, with additional healthcare costs ranging from $35.7 to $45 billion annually.

Moreover, the shortage of skilled surgeons is another thing. The data says there will be a shortage of 19,800 to 29,000 surgeons by 2030.

This requires an emergence and more adoption of AI-led robotic surgery use cases.

AI-powered robotic surgery equipment combines AI with computer vision, 3D mapping, and sensor data to help surgeons perform minimally invasive procedures with enhanced control and accuracy.

These AI-guided robotic systems offer real-time feedback, movement stabilization, and enhanced visualizations for minimally invasive surgeries.

Seeing the precision of robotic surgery, many healthcare departments see the potential in this use case. Some of the popular ones include:

• Urology and gynecology surgeries
• Orthopedics for joint replacements
• Cardiothoracic surgery
• Ophthalmic surgery or ocular surgery
• Microsurgery and neuro procedures

The hospitals that have adopted AI-powered robotic surgery systems benefit from:

• Reduced surgical errors and faster recovery times
• Shorter hospital stays and less postoperative pain
• Supports high-precision microsurgeries beyond human capabilities

9. Remote Patient Monitoring with AI & IoT Devices

The nursing staff in healthcare plays an instrumental role in providing hands-on care to patients, such as administering medications, monitoring vital signs, managing wounds, and assisting with daily living activities.

If you see the data, on average, there is 1 nurse to 4-6 patients for general and surgical wards. In the ICU, there’s 1 nurse for at least 2-3 patients, and in long-term care, there is 1 nurse for every 40 patients.

This shortage of nursing staff can lead to inefficient patient care and poor patient experience at your organization. In that case, the adoption of AI and IoT devices in patient care plays a vital role by enabling remote patient monitoring in real time. With this AI and IoT-enabled remote patient monitoring, the shortage of nursing staff for patient care gets filled, whether in hospital care or someone taking home care.

Wearables like smartwatches and other healthcare IoT devices (like glucometers, smart inhalers, etc.) send continuous data to AI systems that monitor vitals, flag early warning signs, and trigger alerts for doctors or caregivers for further processes.

These types of use cases are more important in healthcare domains like:

• Cardiology for arrhythmia detection, blood pressure monitoring, and more.
• Endocrinology for diabetes management
• Pulmonology for COPD and asthma monitoring
• Post-op recovery programs monitoring

Through the implementation of AI and IoT-enabled healthcare remote patient monitoring systems across these domains, they can benefit from:

• Reduced hospital readmissions and ER visits
• Proactive management of chronic conditions
• Real-time health insights for patients

7 Real-World Examples of AI in Healthcare Operations

The use cases of AI in healthcare mentioned above are not just fictional or on-paper solutions; they are also actualized for real-world applications by many leading healthcare organizations. So, let’s have a look at 7 real-world examples of AI in healthcare settings:

1. Mayo Clinic (USA) Leverages AI to Detect Heart Disease from ECGs

Mayo Clinic is a pioneer in adopting the latest technology to improve patient care. With a top-ranked team of cardiologists who treat more than 100,000 adults and children every year, Mayo Clinic is leveraging AI in cardiology research and clinical practices, leveraging 7 million ECGs to analyze vast patient data to identify patients with potential signs of stroke and at risk of heart disease, such as left ventricular dysfunction and atrial fibrillation (AFib).

Surprisingly, it has identified this condition 93% of the time with accuracy, compared to a mammogram’s accuracy of 85% of the time. Not just that, they also have integrated this AI into their patients’ Apple watches to flag weak heart pumps (a potential sign of low ventricular ejection fraction).

Mayo Clinic has not just leveraged this AI imaging use case to detect disease in cardiovascular medicine but also in neurology, oncology, and radiology.

[Source: Mayo Clinic News Network]

2. Mass General Hospital Uses AI for Early Lung Cancer Detection via LDCT Scans

Mass General Cancer Center, well-known for handling everything from the rarest cancers to the most complicated ones, has started using AI to catch lung cancer early.

Though the mandate under public health recommendation for LDCT scans on people aged 50 to 80 consuming tobacco showed a 24% reduction in death from lung cancer, the numbers for patients detected for lung cancer were still climbing. These numbers include even the ones not using tobacco. So, Dr. Sequist highlighted it as a motive behind leveraging AI to accurately predict potential risks across a wider population.

For that, Mass General Hospital teamed up with MIT to research an AI tool called Sybil, which predicts the potential risk of lung cancer from a single LDCT scan. What’s interesting is that it doesn’t rely on patient history or even input from radiologists; it just works directly from the scan and fits into standard radiology workflows.

[Source: Mass General Brigham Newsroom]

3. Stanford Medicine uses AI for Drug Repurposing Research

Stanford Medicine identified that nearly 5 million deaths are linked to antibiotic resistance globally every year. So, to tackle this, in collaboration with McMaster University, Stanford Medicine developed SyntheMol, a generative AI model that creates entirely new antibiotic compounds beyond existing databases. This model is trained on 130,000 molecular building blocks and a set of validated chemical reactions.

Leveraging training data and advanced algorithms that sifted through 100 million known compounds was able to generate around 25,000 possible antibiotics with their making processes in less than 9 hours.

SyntheMol generated around 70 molecular candidates. Out of those, 58 were synthesized, and six showed strong activity against Acinetobacter baumannii, a deadly, drug-resistant bacterium.

Two of those compounds not only worked but were also water-soluble and passed toxicity tests in mice. Some showed effectiveness against other dangerous pathogens like E. coli, Klebsiella pneumoniae, and MRSA.

This speeds up early-stage drug discovery, offering a powerful path when drug repurposing falls short.

[Source: Stanford Medicine News Center]

4. UK’s NHS Employs AI-Powered Virtual Assistants for Patient Triage and Appointments

In 2024, there were around 300 deaths per week estimated in A&E departments due to long waits. (The Royal College of Emergency Medicine)

Moreover, the existing systems left patients confused, as they were giving mixed messages. For example, public-health campaigns mentioned in it were shepherding patients to consult the Pharmacy first, while risk-aware 111 algorithms were making patients see their GP or go to A&E. Not just that, the older system was mentioning the common “8 a.m. scramble” for GP slots and used to dispatch ambulances for non-critical patients, which was wasting resources.

The inconsistency between services for NHS 111, 999, and GPs resulted in incompatible and inconsistent assessments, repeated processes, delays in care, overburdened staff, and poor patient experiences. Moreover, the frontline assessments are often rigid and impersonal, hard to update or customize, and ineffective in adapting to patient nuances.

Then, the UK NHS saw the TBI analysis that showed the promising impact of investing in AI across patient navigation services, which could free up 29 million GP appointments yearly and achieve productivity gains of £340 million a year for non-clinical workers via GP and NHS 111 services.

So, they are adopting AI solutions (more like virtual assistance) to modernize the healthcare system to automate end-to-end patient navigation and augment clinical decision-making with patient triage, establish a procurement plan, and more. 

[Source: Tony Blair Institute for Global Change]

5. Blue Cross Blue Shield Utilizes AI for Claims Review and Fraud Detection

Fraud in the healthcare system can cost billions of dollars, about 3-10% of spending each year. The majority of healthcare fraud cases are not caused by hospitals or physicians’ billing errors but more by criminal organizations.

“These criminal organizations buy health data on the dark web, pose as clinicians, and then submit fake health insurance claims using the data,” said Jennifer Stewart, senior director for fraud investigation and prevention at Blue Cross Blue Shield of Massachusetts.

Hence, Blue Cross Blue Shield has decided to put AI to work to stop criminal networks to save customers and members millions of dollars in the process.

With this AI solution, they can complete the claim fraud assessment tasks in a day that used to take weeks to do manually. Moreover, nearly 65-70% of the leads gained from the algorithm have been confirmed fraud and abuse cases. Slowly, this algorithm will help the system to learn and evolve with time and get more precise in identifying healthcare claim frauds.

[Source: News Service of Blue Cross Blue Shield of Massachusetts]

6. MD Anderson Cancer Center Personalizes Cancer Treatment Plans with AI

MD Anderson Cancer Center is a global leader in cancer care and research. It is leveraging AI to personalize oncology across clinical operations and patient care. At the forefront of CNS malignancies and computational imaging, Dr. Jingfei Chung is using AI and imaging-based modeling to detect tumors, evaluate treatment responses, and reduce toxicity.

They are exploring AI-driven initiatives to enhance both care and efficiency. Some of the focus AI initiatives include:

• A Generative AI pilot that listens to doctor-patient conversations and transcribes the consultation notes for the EHRs. This is helping physicians spend more time with patients, reducing their administrative burden.
• Predictive analytics analyzes health data trends to identify individuals at high risk of falling and support timely interventions in hospital settings.
• Deep learning to virtually extend its radiation therapy expertise to underserved areas that lack access to specialized cancer care.
• Natural language processing scans provider notes and patient data to accelerate clinical trial matches, and that too, more accurately.
• AI algorithms that predict surgical durations and outpatient care times, leading to improved workflows (with auto surgery scheduling) and reduced patient wait times.

All of it comes back to one thing: making care more personal, more efficient, and available to more people. And that too, ensuring interoperability in healthcare functions.

[Source: MD Anderson Cancerwise Story]

7. Kaiser Permanente Uses AI and Remote Monitoring to Track Chronic Patients

The Mid-Atlantic Permanente Medical Group (MAPMG) is shifting care from clinics to homes using advanced telemedicine. Their remote patient monitoring program uses portable devices to track heart rate, blood pressure, and oxygen levels and even detect risks of stroke or heart attack.

Cardiologists at MAPMG use this tech to monitor patients with chronic heart conditions. One system tracks sudden weight gain through smart scales to catch early signs of fluid retention. Another captures heart activity throughout the day, unlike standard ECGs, which offer only a snapshot.

Following its success, they launched a similar program for patients with high blood pressure. The goal isn’t just to reduce hospital visits but to help patients stay actively involved in their own care. And in many cases, this kind of real-time, at-home monitoring can be lifesaving.

[Source: Corporate Communication by KRproud Mid-Atlantic States]

Final Thoughts on Using AI in Healthcare

There’s a lot happening in healthcare right now, and AI is clearly playing a bigger role than ever before. What’s interesting is that it’s not just happening in labs or R&D teams; hospitals, clinics, and care teams are using AI in very real, practical ways.

We’ve looked at some powerful examples, from diagnostics to remote monitoring, and chances are. A few of them made you think about your own setup. Maybe there’s room to automate more or use data in a smarter way.

In short, it’s worth exploring what AI could look like inside your organization on your terms.

Make Your Healthcare Operations AI-Powered With MindInventory

If you’ve been thinking, “We should be doing something with AI,” but haven’t figured out where to start, then you’re not alone. A lot of healthcare teams feel the same way. The truth is, AI isn’t some distant, complex tech anymore. It’s already solving real problems in hospitals just like yours, and the longer you wait, the wider that gap gets.

MindInventory, as a leading AI development company, doesn’t just build AI tools but helps healthcare organizations understand what’s worth building and why. Sometimes, that’s automating the small things that slow your team down. Other times, it’s a full solution that changes how care is delivered.

FAQs on AI in Healthcare