How AI Can Help Health Professionals Perform Better: Benefits and Success Stories

Compared to other industries, the adoption of AI in healthcare has been rather slow. It’s not surprising: healthcare data is highly complex, often unstructured, and strictly protected by privacy AI regulations, which makes it difficult to develop suitable AI solutions and train them properly.

On top of that, the healthcare industry has always been relatively conservative, and for a good reason. Healthcare professionals are responsible for their patients’ well-being and lives, making them extremely cautious about new technologies' risks.

Still, AI adoption in healthcare is gradually accelerating, with the technology getting more advanced and better adjusted to the data-sharing barriers of the industry. In 2022, the global artificial intelligence technology in healthcare market sized was valued at $15.4 billion. From 2023 to 2030, it is expected to grow at a compound annual growth rate (CAGR) of 37.5%.

In this article, we go over the most influential benefits of integrating AI into healthcare, explore different types of AI technologies used in the industry, and share the success stories of healthcare professionals who have already adopted them.

Benefits of AI technology in healthcare

Benefits of AI technology in healthcare

Artificial intelligence is quite a vague term on its own, and numerous interrelated technologies fall under its umbrella. So, before getting into the perks of integrating AI into healthcare systems, let’s determine which technologies are exactly used in this sector.

Most commonly, healthcare facilities and health-tech services use machine learning, particularly deep learning, for training AI algorithms based on health-related data and preparing it to perform various tasks.

Natural language processing (NLP) is also widely applied in healthcare for understanding human language and thus training AI-powered systems to interpret documentation, reports, and so on. Last but not least, robotic process automation (RPA) is used to streamline administrative and clinical processes.

So, why are these technologies worth exploring if you work in the industry? These three most significant benefits of AI in healthcare will help you figure it out.

Improved diagnostic accuracy

According to 2022 research by Agency for Healthcare Research and Quality, 7.4 million misdiagnosis errors are made yearly in the US alone. In 2.6 million of these cases, patients face damage that could have been prevented. Additional 370,000 errors lead to permanent disabilities or deaths.

Such alarming statistics prove that misdiagnosis is among the most pressing issues in modern-day healthcare, and artificial intelligence can be a major part of the solution.

Misdiagnoses often happen due to a lack of a patient’s medical history or healthcare professionals simply being too strained with numerous patients under their care. AI technology can analyze years of medical records and find relevant information based on the case much faster than humans can.

Moreover, certain AI solutions in healthcare are able to detect life-threatening diseases early on, predict the likelihood of certain patient outcomes, and thus help doctors create accurate treatment plans quicker and more efficiently.

Personalized patient care

One of the biggest advantages of artificial intelligence in healthcare is the quick exchange of information between healthcare professionals. AI solutions can efficiently monitor and analyze patient data, including medical history data and a patient’s lifestyle, thus freeing practitioners time to create personalized treatment plans.

AI is also used in precision medicine, meaning its algorithms can analyze a patient’s genomic data to identify genetic mutations or biomarkers and thus make predictions about the response to certain treatments. This can help healthcare providers tailor treatment plans to individual patients and improve treatment outcomes.

Additionally, AI-powered solutions in combination with IoT (sensors, AI wearable technology, etc.) help healthcare professionals improve real-time patient monitoring by sending alerts in case of any changes or issues.

This last influence on personalized care is less obvious, but AI technology in healthcare is used to analyze medical patients with similar symptoms or diagnoses to better understand correlations and patterns of medical conditions.

In addition to better medical responses, this allows practitioners to have informed and detailed conversations with patients about the nature of their conditions and thus build better, trustworthy patient-doctor relationships, which is extremely important for patient emotional comfort.

Cost savings

The most evident financial benefit of using AI in patient care is probably from the automation of administrative tasks. It doesn’t require such costly investments as robotics technologies do, but still helps direct practitioners’ time and resources towards patient care, which leads to better patient outcomes and increased revenue. However, that’s not all.

Misdiagnoses, improper patient care, and inefficient use of resources cost healthcare providers money. According to a report on malpractice claims among US physicians between 1992-2014, the average compensation for such claims is around $329,565.

And as we’ve established previously, AI in medicine helps doctors create more accurate and effective treatment plans, leading to better patient outcomes and reduced risks of misdiagnosis, malpractice, and lower compensation expenses.

Examples of AI in healthcare

Examples of AI in healthcare

There are so many AI-powered solutions for the healthcare industry trending right now that it’s been truly difficult to single out just five of them. However, we picked the most talked-about options, all of which differ in terms of the budget needed for implementation but are nonetheless worth exploring.

Automation of administrative tasks and workflow

AI-powered solutions can be used to streamline administrative tasks connected to almost every aspect of a healthcare organization’s operations. Just a few examples include automating appointment scheduling through healthcare chatbots, medical billing, patient triage, inventory management, and EHR management.

To give a more explicit example, let’s dissect an example of patient triage. After the patient’s data has been received, the AI tool can analyze their symptoms and previous medical history to determine the severity of their condition and the healthcare urgency level. This way, patients in critical condition can be marked as high-priority and receive the care they need earlier than non-critical patients.

AI can also be used for automating medical transcription, which allows healthcare professionals to spend more time directly taking care of the patient and thus improve patient outcomes and satisfaction.

Read more: How to Develop a Doctor Appointment App for a Clinic: Benefits and Key Features

Clinical decision support systems

CDSS are complex software systems that assist healthcare professionals with decision-making by analyzing patient information and coming up with evidence-based treatment recommendations. Such systems are usually based on multiple technologies, including knowledge-based systems, ML algorithms, and NLP systems.

To give you an example, DXplain is a CDSS developed by the Massachusetts General Hospital to assist healthcare professionals with making accurate diagnoses. The system compares patient data with its medical knowledge data to create a list of potential diagnoses, recommendations for additional medical tests, and suitable treatment plans.

Virtual nursing assistants

Virtual nursing assistant

As of May 2023, one-third of US nurses plan to quit their jobs. This will naturally lead to an even worse nurse shortage, which the industry has faced since the pandemic. Virtual nursing assistants, which often combine technologies like ML, NLP, computer vision, and data processing functionality, might be the right solution to this challenge.

This AI-powered tool, typically an app, can communicate with patients via NLP-based chatbots, obtain information on the symptoms, and provide guidance on the treatment strategy, especially when dealing with chronic conditions.

Such apps can also monitor a patient’s condition remotely, check treatment plan adherence, and alert the healthcare professional when direct care is required. In addition to reducing the extreme workload healthcare workers have to deal with, virtual nursing assistants can work 24/7 and help healthcare facilities cut costs while improving patient outcomes.

One of the excellent examples of this technology is Care Angel. This virtual nursing assistant can be connected to a voice-enabled device like Amazon Echo or Google Home to interact with patients with chronic conditions and thus improve their quality of care.

In addition to providing support to patients and their caregivers, the assistant collects valuable data and insights for healthcare professionals and helps them increase the effectiveness of their care plans.

Medical imaging analysis

No matter how qualified the doctor is, there is still always the risk of human error when reading patient scans. Moreover, certain signs are simply too tiny to be spotted by a human eye, and that’s why it makes sense to get a second opinion from the AI tools and improve diagnostics accuracy.

For example, a few years ago, Fujifilm SonoSite developed an AI-driven ultrasound system called SonoSite Synchronicity. It uses deep learning models to analyze ultrasound images in real time, provides measurements of different anatomical structures, and helps healthcare workers make more accurate diagnoses.

The company continues to improve the product and upgrade it to meet the needs of medical professionals working on the front lines of medicine.

In addition to improved diagnostics, using AI in medical images help doctors predict a patient’s response to treatment, and provide other valuable insights into the nature of certain conditions.

AI-assisted robotic surgery

The da Vinci surgical technique


The reason healthcare facilities are adopting this technology lies in the fact that robotics enhanced with artificial intelligence is simply more precise when compared to traditional surgical practices.

AI-powered robots can be programmed and trained to perform perfectly precise movements, which is especially useful when conducting dangerous surgeries, like neurological ones. For example, the da Vinci surgical technique is often used to perform minimally invasive procedures, such as laparoscopic surgery.

By the way, it doesn’t exclude human surgeons from the process. In fact, the system involves a robotic arm equipped with surgical tools and a camera, which is completely controlled by a surgeon operating a console.

Overall, the AI-assisted robotic surgery approach reduces the risk of human error, improves safety, and in some cases, even leads to faster recovery, since robotic surgery is typically minimally invasive.

Real-life success stories of using AI in healthcare

Due to multiple challenges of AI connected to data security, bias, and lack of transparency, the healthcare field has been extremely cautious about adopting artificial intelligence. However, with the technologies getting more advanced and tailored to specific industries, more and more healthcare facilities embrace AI to improve treatment outcomes.

Here are three inspiring stories of medical facilities and health-tech companies successfully using artificial intelligence for the benefit of patients.

Hungarian clinics using AI to detect breast cancer

As with any disease, the earlier breast cancer is detected, the more chances patients have to recover. However, as the US National Cancer Institute estimates, about 20 percent of breast cancer cases are missed during screening mammograms. Naturally, this puts even more pressure on doctors, specifically radiologists, who are swamped with patients. And once again, AI may come to the rescue.

In 2021, five hospitals and clinics in Hungary, which perform over 35,000 screenings each year, adopted AI systems to help check for signs of breast cancer that might have been overlooked.

The screening system was developed by Kheiron Medical Technologies, an AI software company. The creators fed millions of mammograms to the AI and engaged radiologists to teach the algorithm to detect cancerous growths by analyzing shapes, locations, and density.

After testing the model on over 275,000 breast cancer cases, the company claimed the AI technology can detect cancer at least as well as doctors as the second reader of scans. Moreover, additional testing revealed the AI identified more malignancies than humans, increasing the detection rate by 13%.

Since 2021 and as of March 2023, across five MaMMa Klinika sites in Hungary using the AI, 22 cases of AI detecting cancer the radiologists missed have been confirmed, with more cases under review.

In his interview with The New York Times, Dr. András Vadászy, the director of the clinic chain, says: “If this process will save one or two lives, it will be worth it.”

Danish company helping 911 dispatchers

Corti AI

If a person collapses from a sudden cardiac arrest (SCA), their chances of surviving drop 10% every 60 seconds without CPR or defibrillation. That is why it is crucial not to wait for the EMT to arrive but to provide necessary aid immediately with instructions from a dispatcher. The question is, how can SCA be recognized via a phone call?

This problem triggered the creation of Corti AI, a program built by the Danish company Corti SA that has transformed patient consultations via artificial intelligence.

Initially, it used machine learning to analyze a caller's words and the noise on the line. The algorithm was tested on over 150,000 calls. As a result, the software detected cardiac arrests correctly in 93% of cases, compared to the 73% result human dispatchers showed. Moreover, the program came to this conclusion more than 30 seconds faster. Thanks to this, dispatchers were able to quickly walk the caller through performing CPR.

After successfully testing the AI, Corti SA conducted large-scale trials with live calls in countries across the world. They also started to work on training the system to detect other critical conditions.

In 2021, the startup raised $27 million in series A funding to improve patient consultations. Today, the AI program continues to help healthcare workers with real-time decision support by listening, transcribing, guiding, and coding patient encounters of all kinds.

Duke University hospitals battling sepsis

According to Global Sepsis Alliance, sepsis affects between 47 and 50 million people yearly, and not just in developing countries. In the US, sepsis leads to more deaths than opioid overdoses, prostate cancer, and breast cancer combined every year. On top of that, nearly one in three patients who die in a hospital has sepsis, which starts developing before they arrive in 87% of cases.

At the same time, while deaths from sepsis can be prevented with quick diagnosis, it can be tricky to detect since its symptoms are common for other illnesses. That is why in November 2018, the emergency department of the Duke University Health System released the first version of Sepsis Watch, a deep learning tool created to help healthcare practitioners spot early signs of the disease.

It took the team, which engaged doctors and nurses in the process, 3,5 years to make this product. They trained the AI model with over 32 million data points from more than 42,000 inpatient cases. As a result, this seemingly simple iPad app reviews patients’ data every hour to determine the probability of them developing sepsis and flags high-risk cases.

In the following years, the team has been improving and testing Sepsis Watch on the premises of three Duke hospitals. One of the biggest challenges of implementing new AI tools is not the technical development but the social integration.

Integrating innovation into clinicians’ daily workflow through new communication guidelines, training materials, and adjusting to workplace politics takes an enormous amount of effort. And while there is still a long way to go before the Sepsis Watch can become widespread, the fact that it’s being tested in real hospital environments makes it a very promising project.

Future of AI & ML in healthcare

Future of AI & ML in healthcare

The future of AI in medicine isn’t that difficult to predict, at least partially. After all, the technologies we’ve mentioned in this article are yet to be widely adopted and are likely to stay with us, even if in a more evolved form.

The most promising areas for AI solutions to be part of include precision medicine, drug discovery, remote patient treatment, robotic surgery, imaging analysis, and more efficient management of EHR systems. In other words, the future of AI in healthcare holds nothing you haven’t heard of before.

However, there is an aspect of artificial intelligence adoption that many healthcare organizations overlook: our approach to the development and deployment of AI systems in healthcare. Here is what Dr. Gianrico Farrugia, president and CEO of Mayo Clinic, which serves over 1.4 million patients each year, has to say about it:

“The traditional pipeline model relies on a linear series of points, from coming up with new ideas to turning them into stand-alone products which providers and patients then get to use. Instead, a platform approach relies on an ongoing ecosystem of collaboration. We need to bring together providers, medical device companies, health tech startups, patients, and payers to co-create integrated solutions through digital platforms – based on longitudinal patient data and algorithms that continue to learn over time.”

Additionally, Dr. Gianrico Farrugia highlights the importance of protecting the privacy and security of sensitive patient data when adopting AI technology by using a federated data infrastructure.

“Rather than sending data to AI models, we are bringing the AI models to the de-identified data, creating a ‘glass wall’ that gives external collaborators access to results without the data ever leaving the platform.

Find out more: Top 7 Healthcare Technology Trends in 2023

Cost savings AI solutions in healthcare can lead to

In the section about benefits, we’ve already mentioned that AI solutions can help both healthcare facilities and patients save money. But how much cost savings are we actually talking about?

According to a 2023 paper on the potential impact of AI on healthcare spending, wider adoption of AI could lead to savings of 5% to 10% in US healthcare spending. This roughly translates to $200 billion to $360 billion annually in 2019 dollars within the next five years.

The cost savings mentioned cover hospitals, health insurers, and private payers. Hospitals, for instance, could save between 4% to 11% of their total costs by using AI in medical billing systems and clinical operations. As for the private payers, they could save somewhere between 7% to 9% of their total spending.

There also are finance-related benefits for hospitals that are harder to calculate but are nonetheless extremely important. Healthcare systems across the world are facing a crisis, with hospitals reducing the number of crucial services like inpatient care or shutting down completely.

Moreover, many healthcare facilities are struggling with a personnel shortage, which worsened during the COVID-19 pandemic. AI solutions can be a way for hospitals and clinics to automate procedural tasks and free their healthcare professionals to focus on patient treatment, which is of primary importance.

Together with AI-driven resource optimization tools, care automation could potentially help hospitals treat more patients with the same amount of personnel, provide a better patient experience, and thus increase revenue.

Read more: Medical Web Development: Building a Trendy Up-to-Date Medical Website


With the increasing prevalence of chronic diseases, changing lifestyles, and technological advancements in the industry, modern-day healthcare focuses on preventive care and early diagnosing to meet patient expectations. And the role of AI and ML algorithms is crucial for such an approach, not to mention the financial benefits they can bring healthcare providers.

Understandably, investing in robotics and other complex technologies can cost an impossible amount of money for small or underfunded healthcare facilities. However, an AI project aimed at automating administrative tasks or integrating virtual nursing assistants can be more than realistic, especially if delivered by an outsourcing company with a reasonable rate.

If you are looking for ways to streamline your organization’s workflow through artificial intelligence — Mind Studios is ready to guide you through possible implementation strategies that would suit your budget. Fill in a short contact form, and our business development team will schedule a free consultation for you shortly.