10 Ways Artificial Intelligence Benefits Healthcare

The pandemic accelerated healthcare's digital transformation that, some experts believe, was long overdue. Throughout 2021 and 2022, healthcare organizations worldwide were urged to turn to technology. Research led by HIMSS states that 99% of the surveyed US-based healthcare leaders and 95% of their international peers agree that it is crucial for their organizations to actively invest in digital transformation.

Artificial intelligence was one of the techs that brought about substantial change to the way care is delivered, with more medical organizations adopting AI healthcare solutions. The numbers speak for themselves: the global AI in healthcare market is expected to reach $208.2 billion by 2030, growing at a CAGR of 38.4% from 2022.

In the piece below, we shed light on the top ten use cases of AI in healthcare and share tips on how to adopt AI — without adding to the uncertainty of the post-pandemic era.

Top ten applications of AI in healthcare

Robot-assisted surgery

AI-powered robotics, including surgical arms, cameras, and consoles, give surgeons more precision and control. For patients, it guarantees less pain, scarring, and blood loss, as well as a lower risk of infections and a faster return to daily routines.  

One of the pioneers in using surgical assistance tools, Mayo Clinic in Jacksonville, Florida, uses AI to perform abdominal surgeries and is currently developing robotic tools to operate on the brain. To achieve maximum precision, the clinic makes AI-powered robots recognize and replicate the movements of human surgeons.

Maastricht University Medical Center, too, relies on AI-powered robots to stitch up tiny blood vessels, some no thicker than 0.03 millimeters.

These were just a few of the many examples. Besides that, AI-powered robots are widely used in urology, gynecology, cardiology, as well as in general, cardiothoracic, and colorectal surgery. 

Virtual nursing assistants

Virtual nursing assistants help establish a more regular communication between patients and care providers and allow to reduce the number of unnecessary hospital visits. 

By monitoring the data from wearables and patient-facing mobile apps, like vitals, habits, and medication intake, nursing assistants give recommendations to patients and inform medical workers of potential threats to patients’ health. Nursing assistance apps can also prompt a patient to schedule a medical appointment, if needed, or even set one up automatically. 

One example of a virtual nursing assistant is an app called Angel. The voice-powered virtual assistant uses AI to analyze real-time clinical and non-clinical data to increase patient engagement, enhance chronic condition monitoring, and improve population health management. 

The benefits of remote nursing assistants are apparent to both care providers and patients. Among the latter, 64% say they feel more comfortable receiving instructions from a virtual nurse. The reasons for the popularity of such solutions are many, 24/7 access to medical care and the ability to get quick recommendations being the leading ones. 

Administrative workflow automation

AI-powered workflow assistants simplify appointment booking, help discharge patients faster, and allow caregivers to free up the time spent on filling out EHR forms (16 minutes per patient!)

To enable that, such subsets of AI as natural language processing and machine learning come to use. They help users navigate medical records using voice commands, transcribe clinical data recorded during patient visits, and provide personalized responses to EHR searches. 

One example of a clinic relying on AI to automate their workflows is Cleveland Clinic. The medical institution relies on IBM’s Watson Health to enhance patient care across nine regional hospitals and 18 full-service family health centers. Analyzing mass amounts of clinical and administrative data, as well as medical literature, the solution is able to support clinical care and automate a larger share of administrative tasks.

Fraud detection

In the US, around 3% of healthcare claims are fraudulent, which equals a hundred billion dollars lost annually. Luckily, AI-powered solutions can automatically scan insurance claims and detect invalid ones before they are paid for. AI-based software helps speed up claims processing, approval, and payment, too. 

In addition to detecting insurance fraud, AI can flag erroneous billing transactions, for instance, when a patient is billed for a procedure they did not receive or when they are billed for a simpler procedure as for a more complex one. 

The benefits of AI for fraud detection span lower costs of care and lower premiums for patients, as well as overall higher patient satisfaction.

Medication error recognition

According to the National Institute of Health, medication errors occur for one out of 131 outpatient deaths and one out of 854 inpatient deaths. Medication errors are often attributed to flawed EHR interfaces. Several studies have reported EHRs’ patient identification, drug administration, and prescription unit errors. Artificial Intelligence helps prevent these. 

Intelligent algorithms compare new prescription orders against historical EHR data and flag those that deviate from typical patterns, prompting doctors to review them once again. 

Compared with legacy clinical decision support (CDS), AI-based tools show significantly higher specificity and accuracy. A trial study of MedAware, an AI-powered solution designed to recognize prescription errors, found that the solution only flags 0.2% to 0.5% of all prescriptions, with about 75-80% being true positives and 35% — false negatives. In contrast, CDS can only identify a small share of erroneous prescriptions since they’re rarely patient-centric and not as adaptable.

Diagnostic assistance

AI-powered tools do not only increase medical imaging departments’ productivity and reduce the cost per exam, but also support diagnostic confidence. 

Computer vision, one of AI subsets, helps diagnose tumors, bone fractures, tuberculosis, strokes and chronic heart diseases, liver and kidney infections, and other conditions by accurately flagging abnormalities in all kinds of medical images — from CT to MRI to ultrasound.

For example, a recent study published in Lancet found that analyzing brain MRI scans with AI has the potential to identify tissue changes reflective of early ischaemic stroke within a narrow time window from symptom onset with a greater sensitivity than a human reader.

AI proved to be as and even more accurate than a human doctor in detecting brain, prostate, breast, lung, and other types of cancer, as well as cervical precancer.


The healthcare industry has always been prone to cyberattacks. The WannaCry ransomware paralyzed parts of the UK's national health services for days back in 2017. Two years later, a malicious agent compromised the personal information of thousands of HIV-positive patients in Singapore. And today, the COVID-19 fallout is giving healthcare cybersecurity a whole new degree of importance. 

To prevent data breaches, downtimes, and reputational losses, healthcare organizations are adopting AI-powered security tools. 

Boston Children’s Hospital, for example, uses AI-based tools to flag unusual behavior in real time, say, hundreds of doctors trying to simultaneously access a patient’s record. 

To identify what behavior is normal and abnormal for each user, AI algorithms analyze data flows within a healthcare organization. Using this knowledge, AI can then pinpoint and mitigate cyberattacks before they cause any harm to the organization’s technology systems. 

Connected medical devices

Artificial intelligence powers a whole range of connected medical devices, from glucose monitors to sensor-equipped wearables monitoring one’s vitals to more complex insulin delivery systems, and more. AI interprets data generated by these devices, thus, providing a real-time look into a patient’s health. 

Philips Healthcare’s IntelliVue is an example of a connected medical device powered by AI that gained popularity on the market. The solution monitors patients’ vitals and informs doctors once a deviation is identified. Seventy percent of caretakers claim IntelliVue has made it easier for them to spot patients in need of immediate assistance.  

Identifying clinical trial participants

Recruiting clinical trial participants is one of the most time-consuming parts of clinical research. AI is capable of finding the right candidates in a matter of hours. 

By utilizing vast volumes of data, including that from wearables, electronic medical records, and pathology reports, AI algorithms quickly single out patients eligible for medical trials, thus, dramatically speeding up medical research, making experimental treatments more efficient, and cutting down the cost of medical research.

It is still rare for AI to be an integral part of clinical trials, but some institutions have already seen its value. Using AI tools, researchers at Cedars-Sinai Smidt Heart Institute in Los Angeles identified 16 eligible participants for their trial in just one hour, while a manual search yielded just two candidates in six months.

Fostering diagnosing and treatment

AI algorithms can analyze patients’ symptoms, medical images, and the data from wearables against available treatment options, side effects, previous research mistakes, and conditions with similar symptoms to give a preliminary diagnosis and come up with optimal treatment strategies.

Since AI can process a lot of data points in parallel, it can and sometimes does outperform humans in diagnosing diseases. For instance, Moorfields Eye Hospital in London uses artificial intelligence for diagnosing eye conditions. The software in use diagnoses and offers personalized treatment options for over 50 ocular conditions and diseases with an accuracy rate of 94% which is on par with the performance of top healthcare professionals. 

Things to keep in mind when adopting AI in healthcare

AI initiatives can be complex and time-consuming, especially in sectors as regulated as healthcare. Here are the key things to think out before turning to medical software companies and jumping on the AI bandwagon. 

  1. Only move forward with an established use case in place. Start by integrating AI into routine, recurring tasks that provide an opportunity to increase efficiency. Later, you could build on that to establish more use cases. It is also crucial to hold interviews with doctors, patients, and hospital staff before getting approval from the executive board. It will allow you to detect clinical and operational bottlenecks and identify ways in which technology can solve them. Another aspect to pay attention to is assessing the quality and quantity of data points on hand. It’s important to get a clear picture of the available data volumes and, if needed, develop data sourcing strategies before moving on to the technical aspects of implementing AI.
  2. Ensure the C-Suite and patients are on board. The use of AI in healthcare is still relatively limited. So, it’s only natural for healthcare executives to doubt its ROI and reliability. The doubt usually stems from a lack of understanding of how AI works. To get a buy-in from the executives and patients, build on the established use case and state the process and economic value AI is going to bring. We also recommend sticking to AI explainability principles to help potential users understand how exactly AI algorithms make decisions. Patient data security and a human-in-the-loop approach, that is making sure a human retains control over AI’s decisions, are things to be regarded as well. 
  3. Identify and resolve technology-related hurdles. Adopting AI in healthcare — no matter if you opt for a ready-made solution or decide to craft one from scratch — you have to prioritize reliability, security, transparency, and compliance. Put down respective strategies early in the project to ensure the technology and design choices that you make reflect those aspects.
  4. Educate the personnel. Working with AI will require doctors and nurses to acquire new skills. To teach the medical staff on how to work alongside machines so that they do drive value, include a comprehensive training program into your organization's AI adoption plan.