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Benefits of AI in healthcare: from diagnosing patients to automating workflows

By Victoria Shashkina, Innovation Analyst
Published on

Years have passed since the pandemic, but the healthcare sector is not seeing any relief. About half of its employees are experiencing burnout. There is too much meaningless manual work and too little time to spend with patients. Medical facilities want to rely more on technology, but then cybersecurity and data privacy issues come into play. Is there a solution that can tackle it all?

Building on automation, artificial intelligence is revolutionizing healthcare and helping caregivers address the challenges they face.

So, what does the future of AI in healthcare look like? Will artificial intelligence replace doctors? What are the specific examples and benefits of AI in healthcare organizations? And how do you implement AI solutions for healthcare without adding more uncertainties? Let’s dig in.

Top 10 AI examples in healthcare

AI has many exciting contributions to the healthcare sector. Some AI in healthcare solutions help doctors with patient diagnosis and treatment, while others automate tedious tasks in the facility’s workflow.

ai in healthcare

Robot-assisted surgery

Many fear that robot-assisted surgery involves AI replacing doctors and making autonomous decisions about incisions and other factors. In reality, it’s a human who stays in control, while AI-powered robotic instruments help surgeons make more precise and delicate motions.

Large hospitals are experimenting with this technology. Tampa General Hospital has already adopted robot-assisted surgery, along with Mayo Clinic, Johnson Memorial Hospital, and Cleveland Clinic, to mention a few. The last one claims that this approach to surgery lowers infection risks, causes less pain, and results in shorter hospital stays.

Benefits of robot-assisted surgeries

  • Surgeons can consult their less experienced colleagues without having to be physically present

  • Less pain and lower infection risks, as reported by Cleveland Clinic

Preliminary diagnosis and disease detection

AI in healthcare feeds on the data about previous diagnoses and learns to make diagnostic decisions, just like doctors do. AI algorithms take in symptoms data, the data from wearables, and any other medical history information and analyze it against previous research mistakes, available treatment options, side effects, and diseases with similar symptoms to deliver a preliminary diagnosis.

AI-powered tools can have the following approaches to diagnosing patients:

  • Physical examination-driven diagnosing

    Doctors can incorporate AI models into the tools they use during physical examinations. For example, a research team at Moorfields Eye Hospital and UCL Institute of Ophthalmology built comprehensive AI software that can spot any sight-threatening conditions and predict other significant health issues, such as heart attacks.

  • Diagnosing based on medical images

    There is enormous potential for AI in radiology. It can enhance poor-quality medical images, create 3D models, and enable secure storage and sharing of healthcare information. AI-powered medical imaging solutions can also detect abnormalities, such as tumors and hidden fractures.

    For instance, scientists at the Beckman Institute for Advanced Science and Technology implemented an AI-powered algorithm that can spot tumors in medical images. What makes their approach unique is that they use explainable AI principles, and their model justifies its outcome and creates a visual map.

    Check out our blog for more information on AI in cancer detection and treatment.

  • Speech-oriented diagnosing

    Natural language processing (NLP) models can spot early signs of cognitive decline. Researchers at the University of Texas explored this opportunity. They experimented with machine learning and NLP to detect voice biomarkers, indicating early onset of Alzheimer’s. With this method, doctors only need to spend ten minutes recording each person’s voice instead of dedicating several hours to the traditional neuropsychology tests.

Benefits of AI in disease detection

  • AI healthcare assistant can be a source of a second opinion, offering reassurance

  • Catching diseases during early onset, when the symptoms are not yet visible to the human eye

Personalized treatment recommendations

AI models can analyze different types of patient data and recommend personalized treatment options or adjust the treatment course for optimal results. Here are two examples from our portfolio.

We have developed an AI-powered platform that runs accurate power calculations for lenses implanted in patients as part of a treatment for cataracts, myopia, and other eye conditions.

Another example of this AI in healthcare use case is an AI platform designed to collect, manage, and present data for patients diagnosed with cancer. The platform features a predictive analytics and decision support system that recommends treatments and generates survival curves for newly diagnosed patients based on factors such as patient age, gender, comorbidity, cancer site, cancer stage, and tumor grade.

Benefits of AI-powered personalized treatment recommendations

  • Increasing patient satisfaction and engagement

  • Suggesting treatment options that are tailored to individual needs and have a higher success rate

Virtual nursing assistant

89% of patients in the US google their symptoms before turning to doctors, and the results of such self-diagnosing turn out rather scary. Virtual nursing assistants prevent such delusive endeavors. Using AI healthcare analytics, they monitor patients’ health parameters, medication intake, and habits to prevent the worsening of chronic conditions and schedule medical appointments when needed.

A good example of a multi-functional virtual nurse is Molly. Visualized as an avatar, it provides remote support for common medical conditions and tracks a patient’s weight, blood pressure, and other parameters generated by monitoring devices. The app features a chatbot, too, for patients to discuss their health requests privately and book an appointment with their physician.

Benefits of AI healthcare virtual assistant

  • Patients have 24/7 access to medical care

  • Doctors and nurses get a break from administrative tasks like appointment scheduling

Drug prescription and administration error detection

Adopting artificial intelligence in healthcare enables medical professionals to catch any prescription, dosage, and administration errors before they cause serious harm. Here is how it works.

Prescription errors

In the US alone, around 9,000 people die annually because of prescription errors. These errors often stem from flawed EHR interfaces—doctors choose wrong drugs from a drop-down menu or get confused in dosing units. Adopting AI in healthcare can fix this issue. AI algorithms analyze historic EHR data and compare new prescriptions against it. The prescriptions that deviate from typical patterns get flagged, so doctors can review and adjust them.

Brigham and Women’s Hospital uses an AI-powered system to pinpoint prescription errors. Over a year, the system identified 10,668 potential errors, and 79% of them were clinically valuable, so the hospital managed to save $1.3 million in healthcare-related costs.

Drug and medical device administration errors

AI can monitor medication intake and alert the corresponding doctor when a patient can’t self-administer it correctly. For example, one research team tried to monitor how patients use inhalers and insulin pens with an AI-powered sensor. This device emits radio waves that reflect off a patient’s body. Then, a tailored AI algorithm analyzes the reflection and catches on any errors in self-administration.

Benefits of AI in prescription error detection

  • Preventing drug overdose and other health-threatening conditions

  • Monitoring patients’ medication adherence

Connected medical devices

AI serves as “the brain” for a whole range of connected medical devices—from simple glucose monitors to advanced insulin delivery systems and wearables for monitoring blood pressure and other vital signs. The AI engine processes the data fetched from connected devices to alert doctors if anything goes wrong and provide real-time reports about patients’ health.

Remote patient monitoring

AI-powered connected medical devices‌ enable healthcare professionals to monitor their patients without dragging them to the hospital. And sometimes, patients just don’t have the possibility to attend a physical consultation. That’s where solutions, like ThinkAndor, are particularly helpful. ThinkAndor is designed to help doctors follow up on patients in rural areas. This comprehensive patient monitoring system includes an AI-driven virtual assistant that gathers patients’ data from dedicated devices, captures self-reported assessments, and transmits all this to the doctor without relying on WI-FI or cellular connectivity, which also might not be present at some locations.

We also built a data-driven diabetes management system to help people keep Type 1 diabetes under control. It analyzes patients’ glucose levels and produces reports that doctors can use for a better informed decision making.

Staff behavior and compliance monitoring

Connected medical devices can also increase compliance among medical staff. Check out this example from our portfolio.

We have implemented AI in healthcare to help our client track the frequency and quality of hand hygiene in hospitals via connected wristbands and a compliance monitoring platform. With an advanced dashboard, hospital administrators could monitor hand hygiene compliance on a daily, weekly, monthly, or even hourly basis. As a result, the hospitals saw an increase in hand hygiene compliance by more than 70% within a single week.

Benefits of AI-powered connected medical devices

  • Monitoring vulnerable patients 24/7

  • Making better clinical decisions thanks to personal medical data supplied by connected devices

Administrative workflow automation

Doctors spend about 16 minutes per patient just to fill out EHR forms. But with AI-powered workflow optimization, caregivers can stop worrying about administrative tasks and dedicate their time to patients. Machine learning, robotic process automation, and natural language processing—and other types and subsets of AI—help navigate medical records with voice commands, transcribe clinical data recorded during patient visits, and return personalized responses to EHR searches. AI-powered workflow assistants simplify appointment booking and help prioritize and discharge patients quicker as well.

We at ITRex have helped caregivers optimize daily operations by developing an intelligent process automation solution that synchronizes appointment scheduling between an appointment booking service and an EMR. The solution frees doctors from manually entering appointment data into two systems and adds consistency to patient scheduling.

Benefits of AI in healthcare workflow automation

  • Operational costs decrease as medical employees don’t have to deal with tedious manual tasks

  • The quality of care improves as doctors can dedicate more time to patient care

Fraud detection

Studies show that fraud in healthcare stands at a whopping $380 billion per year. And about 3% of all healthcare claims in the US are fraudulent. Not that big a number, but it translates into a hundred billion dollars lost annually. Artificial intelligence automates claims assessment. Machine learning models flag invalid claims before they are reimbursed and help speed up the processing, approval, and payment of valid ones.

But it’s not only insurance fraud that AI is capable of detecting. When it comes to billing for procedures a patient never received, upcoding—billing for a simple procedure as for something more complex—or preventing patient data from being stolen, AI helps there, too.
One example of such a comprehensive system comes from Mastercard. The company offers AI-powered MastercardⓇ Healthcare Solutions that help insurers detect fraudulent claims. These AI models can be adapted to focus on prescription abuse, phantom billing, and other issues.

Benefits of AI in healthcare fraud detection

  • Faster insurance claim processing

  • Better insurance rates for patients

Patient readmission prediction

Studies dating back to 2015 showed that Centers for Medicare and Medicaid Services waste around $26 billion annually on readmissions. Since then, the government has been trying to remedy the situation by fining hospitals for excessive readmissions through initiatives like the Hospital Readmissions Reduction Program. Over the last ten years, they penalized 93% of the participating acute care hospitals. Healthcare organizations are turning to technology to keep discharged patients out the door.

In a 20-month long experiment, Corewell Health used AI and clinical data to spot patients at risk of readmission and give them more extensive discharge support and frequent follow-ups. An interdisciplinary team proactively reached out to every high-risk patient to make sure they stayed on track. This approach saved the organization $5 million in readmission costs.

Benefits of AI in readmission prediction

  • Lower readmission penalties

  • Higher patient satisfaction


The healthcare sector isn’t new to cyberattacks. Back in 2017, the infamous WannaCry ransomware paralyzed parts of the UK’s National Health Service for days. In 2022, OneTouchPoint (OTP), a healthcare mailing and printing vendor, endured severe exposure. OTP’s large clients, such as Kaiser Permanente and Blue Shield, started reporting data breaches, and 2.6 million people were affected. And these are just a few examples.

AI-based security solutions analyze data flows within a technology system to get a grasp of what behavior is normal and abnormal for each user. Building on this knowledge, AI detects and neutralizes cyberattacks, so attackers are caught before they do any damage to the system. For instance, a group of researchers experimented with machine learning to detect cyberattacks on medical IoT devices. The team engineered 33 types of cyberattacks on the equipment, and the ML tool managed to spot anomalous traffic with 99.55% accuracy.

Along with preventing cyberattacks, AI also protects healthcare data. For example, we used AI to mask huge volumes of personally identifiable information across our client’s databases, cloud apps, and unstructured resources so that they could achieve compliance with healthcare standards.

Benefits of AI in healthcare cybersecurity

  • Preventing harm to a hospital’s reputation and avoiding lawsuits

  • Protecting patient data

A path to reaping the benefits of AI in healthcare

The road toward AI can be bumpy. But having led many AI implementation projects ourselves, we’ve come up with a roadmap that makes AI adoption less stressful and far more effective. Here is what we recommend.

Establish a use case

To realize the benefits of artificial intelligence in healthcare, it is crucial to start right. Begin by incorporating AI into highly repetitive tasks that offer great opportunities to drive efficiency, and build on from there to adopt more use cases.

Before getting approval from the executive board, interview hospital administration, doctors, and patients to detect organizational and clinical problems and identify how technology can improve patient experience and staff productivity.

Also, study available data to understand if it’s enough to train reliable AI algorithms and whether additional data processing will be needed.

Maybe start with an AI proof of concept to test your ideas before proceeding to a full-blown implementation.

Get buy-in from the C-Suite and patients

AI is still new to healthcare. So it is usual for healthcare executives to worry about the ROI and reliability of AI-powered diagnosis and treatment. The worries often stem from a lack of understanding of how AI works and how it drives value.

To get buy-in from hospital executives, clearly state the process and economic value AI is going to bring (it is not too difficult with a well-established use case), supporting it with numbers. AI provides clear advantages here. Deloitte found that 83% of early AI adopters have already achieved moderate to substantial economic benefits.

And to persuade patients, make sure to explain how AI works, prove the efficiency of AI for solving a specific task, guarantee that a doctor will stay in control over AI’s decisions, and prioritize patient data security.

Overcome technology-related challenges

When implementing AI for healthcare tasks, it is important to address AI-related ethical issues and prioritize AI explainability. Whether you choose to go with a ready-made solution or develop one from scratch, make reliability, transparency, security, and compliance a priority. This will help you understand what makes sense to migrate and what your future architecture plan should look like.

Educate employees

The introduction of AI will require new skills from doctors and nurses. To educate the medical staff on how to work alongside smart machines and applications, include comprehensive training sessions in your AI rollout plan.

To conclude

Successful adoption of AI in healthcare largely depends on how experienced and skilled your technology partner is, as well as their readiness to support you during AI deployment and fine-tuning. We at ITRex have vast experience in both artificial intelligence and the healthcare sector. And we have a dedicated R&D department for particularly innovative projects. We’ve already presented several projects from our portfolio throughout this article. Our team is well equipped to support you on your AI journey. Get in touch, and we will be happy to help.

In the meantime, check out our blog to discover how robotic process automation (RPA) benefits healthcare and how the novel Gen AI technology transforms the healthcare sector.

Top 10 AI examples in healthcareRobot-assisted surgeryPreliminary diagnosis and disease detectionPersonalized treatment recommendationsVirtual nursing assistantDrug prescription and administration error detectionConnected medical devicesAdministrative workflow automationFraud detectionPatient readmission predictionCybersecurityA path to reaping the benefits of AI in healthcareEstablish a use caseGet buy-in from the C-Suite and patientsOvercome technology-related challengesEducate employeesTo conclude
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