AI in Healthcare – Use Cases and Benefits

Artificial Intelligence and big data are more commonly used in healthcare every year. With this article, we will take a closer look at both these disciplines and see the benefits of implementing Artificial Intelligence and big data in the healthcare industry. We will also go through the history of Artificial Intelligence and big data in healthcare and its future.

Similarly, as in the pharmacy – Artificial Intelligence is a new trend in the healthcare industry sector and you can easily say that it’s still in its infancy. When most people hear “Artificial Intelligence in healthcare” their first thoughts may be related to the Star Wars movies, where there are no human doctors.

Everything related to healthcare is done by intelligent robots and systems. Is this our future? Well, probably. But we are still far away from that. Let’s stick to the Earth!

What is artificial intelligence in healthcare?

We should start at the beginning!

What is artificial intelligence (AI) in healthcare exactly about?

Take a look at the definition provided by Wikipedia: “is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input”.

To put it in more “human” language – Artificial Intelligence is everything, including applications, systems, algorithms, and devices that help human physicians in providing healthcare, and is based on computer analysis and big data.

For instance: robot-assisted surgery units, diagnostics algorithms, drug research algorithms, devices monitoring patient’s body condition and many more. It is hard to imagine modern medicine without additional artificial intelligence support, even though the way to its real role in the healthcare industry has “just” started.

So long story short: what is artificial intelligence and big data in healthcare? It’s a necessity!

History of AI in healthcare

The history of Artificial Intelligence in healthcare is quite short as it needs many modern inventions to work, not just computers and the internet. The first attempts to implement AI in healthcare were in the late XX century, around the 1970s, when Dendral was introduced at Stanford University, USA. It is assumed to be the very first Artificial Intelligence in the healthcare system.

Originally, it was used to help chemists in identifying unknown organic molecules by analyzing their mass spectra and using knowledge of chemistry. Dendral was written in the LISP programming language and was a father for many following Artificial Intelligence systems in healthcare, to name just the MYCIN – system that used Artificial Intelligence to identify bacteria causing severe infections, such as bacteremia and meningitis, and to recommend antibiotics, with the dosage adjusted for patient’s body weight.

The next milestone happened in around 1990 in the minds of the artificial intelligence systems. They decided that if AI has to offer any assistance, it has to be based on the expertise of physicians and take into consideration the lack of perfect and vast data. That was necessary for the development and improvements of artificial intelligence in healthcare. Since then, much has changed, and today, Artificial Intelligence in healthcare is much more advanced.

Examples of artificial intelligence. How is it used today?

Today, Artificial Intelligence in healthcare brings much more value to the industry. It is developing rapidly and is predicted to do so, or even faster in the near future. We will take a look at the future of AI in healthcare, but first, let’s find out what goes on right now.

Take a look at some examples of how is Artificial Intelligence used in healthcare and check what benefits Artificial Intelligence brings to the healthcare industry.

When did Artificial Intelligence become common in healthcare?

AI in healthcare isn’t new – it’s been around since the 1960s. Back then, we had systems like MYCIN, which helped diagnose infections and suggest antibiotics. But these early AI tools were mostly experimental and didn’t catch on widely. Why? The tech just wasn’t there yet, and we didn’t have enough data to make them work well.

Fast forward to the 1980s and 1990s. Computers got smaller and networks started popping up everywhere. This helped a bit, but AI still wasn’t making big waves in healthcare.

The real game-changer came in the 2000s with electronic health records (EHRs). Suddenly, healthcare providers had a goldmine of data to work with. They started using AI to crunch numbers and help make clinical decisions. By the 2010s, machine learning was all the rage. Doctors were using it for everything from diagnosing diseases to keeping an eye on patients and predicting health trends.

Then came 2020 and the COVID-19 pandemic. Healthcare needed solutions fast, and AI stepped up to the plate. We saw AI being used to track the spread of the virus, develop vaccines quicker, and even monitor patients remotely. This push made the healthcare AI market explode – experts thought it could hit $6.6 billion by 2021.

Today, AI is everywhere in healthcare. It’s helping doctors diagnose diseases, suggesting treatments, handling paperwork, and even chatting with patients. It’s not just a fancy add-on anymore – it’s become a crucial tool for making healthcare more efficient and effective.

Examples of AI in healthcareAccording to a Microsoft-IDC study, 79% of healthcare organizations are currently utilizing AI technology, highlighting its growing acceptance within the industry.

Diagnostics

One of the examples of Artificial Intelligence in healthcare is diagnostics. We wrote about that previously in the article about AI in pharmacy. Diagnostics consist of tons of data – to name just medical imaging analysis, patient medical records, patient treatment history, patient genetics, and his or her circumstances.

In the past few years, AI has become more accurate in identifying disease diagnosis and recommending optimal treatment. The best example is the cancer diagnosis. Standard, radiological methods are not sufficient. As it turns out, traditional radiological imaging misses signals indicating cancer in about 30% cases!

On the other hand, Artificial Intelligence is much more accurate. In 2013 data scientists from the KAIST University in South Korea introduced an Artificial Intelligence algorithm called LUNIT, that’s capable of identifying cancer cells basing on x-rays images and mammography images to detect lung and breast cancer. Its accuracy was mind-boggling 97% in detecting lung cancer and breast cancer.

According to the Accenture company, automated image diagnosis itself can save a whopping $3 billion a year!

Robot-assisted surgery

Take a look at another example of Artificial Intelligence in healthcare – robot-assisted surgery. This is one of the most essential applications of AI in healthcare. In this case, there are two main benefits – huge money savings and more effective surgery.

Accenture estimates that AI robot-assisted surgery could save the US healthcare industry $40 billion annually by 2026.

And what about the surgery itself? Well, as we said, robot-assisted surgery is much more effective and precise. In 2017 alone there were executed almost 700 000 robot-assisted procedures. Thanks to its precision and miniaturization, the results are undisputable – smaller incisions, decreased blood loss, less pain, and quicker healing time.

However, there is the other side of the coin. The robot-assisted procedures are more expensive, as one robotic unit costs at least $1M, and it takes time to properly train surgeons in using Artificial Intelligence support. Surgeons have to perform 100-250 surgical procedures in order to use their new robot assistants for the benefit of the patient*.

Now you know how is artificial intelligence used in healthcare. Let’s turn to the big data.

Benefits of AI in Healthcare

The integration of AI-powered automation and generative AI into healthcare systems can reshape how care is delivered by enhancing efficiency, accuracy, and patient engagement while reducing costs. These technologies promise to improve both patient outcomes and the operational viability of healthcare providers in an increasingly complex environment.

Streamlined administrative tasks

AI automation significantly reduces the administrative burden on healthcare professionals by automating routine tasks such as scheduling appointments, managing patient records, and processing billing. This allows healthcare workers to focus more on patient care rather than paperwork, ultimately improving job satisfaction and reducing burnout among staff.

Personalized patient care

AI in healthcare can tailor treatment plans based on individual patient data, such as medical history and genetic information. This personalization enhances the effectiveness of treatments and increases patient satisfaction, as care becomes more aligned with individual needs[1][6].

Improved patient experience

AI-driven tools like chatbots provide 24/7 support for patients, answering questions and assisting with appointment bookings. This continuous engagement helps alleviate patient anxiety and enhances their overall experience with healthcare services.

Improve efficiency

By automating processes and optimizing resource allocation, AI in healthcare organizations reduce operational costs. For instance, predictive analytics can identify high-risk patients for early intervention, potentially preventing costly complications down the line.

Big data in healthcare

Although big data in healthcare is strictly related to Artificial Intelligence in healthcare, these two disciplines are not exactly the same thing. To simplify it, think of big data as a source for Artificial Intelligence. Big data is exactly what powers up the Artificial Intelligence and allows it to work efficiently.

When you deal with large amounts of data, at some point it becomes very difficult or even impossible to master all of those gigabytes. The data scientists are trying to automate the storage and analysis of these large amounts of data in order to get as many advantages as possible from them.

Big data in healthcare consists of billions of entries about patients, treatments, drugs, surgical procedures, research results, and many more. If you want to use all that data on a regular basis, you simply have to think of the way to analyze and process it efficiently. And this is what big data in healthcare is about.

Now, take a look at the benefits of artificial intelligence and big data in healthcare. Generally speaking, big data can help in improving patient service, determining and implementing appropriate methods for patient treatment, supporting clinical treatment or monitoring efficiency of the healthcare companies.

There is much more, but today we will name and look closer to the three most important benefits.

Benefits of Big Data in healthcare

Much more improved patient care

Thanks to the electronic data records, collecting patient data is much more effective and easier to use to find the best treatment for the given patient. Big data helps in collecting demographic and medical data such as lab tests, clinical data, diagnoses, medical conditions, treatment history, family member’s clinical data, etc. What’s more, big data can help in the prediction of disease incidence or detecting trends that lead to better health and lifestyle of society.

Improved operational and R&D efficiency

Healthcare companies can cut down on healthcare costs and provide better care. All that with the help of predictive analysis of the staff efficiency and patient admissions for example to the hospital. Big data in healthcare helps in organizing workflow and provide not only better care but also more effective in terms of the costs. And what about R&D? Big data is supporting work on new drugs and clinical trials thanks to the ability to analyze all data instead of the selection of the test samples. Big data also has the ability to identify specific patients with wanted biological characteristics who will participate in specialized clinical trials.

Finding a cure for diseases

Big data can help in uncovering earlier unknown disease correlations, hidden patterns, and insights. All thanks to examining large sets of data to find new cures for the diseases or prescribe the best treatment. Big data has the possibility of predicting the occurrence of specific diseases or prognosis of disease progression and factors determining it.

Surely, you will agree that the benefits of big data in healthcare are staggering, creating great new possibilities and perspectives for the future.

Challenges of AI in Healthcare

The integration of artificial intelligence (AI) in healthcare, including machine learning, presents numerous challenges that must be addressed for successful implementation. These challenges span technical, ethical, and operational domains, impacting the effectiveness of AI-driven healthcare solutions.

Data privacy and security

AI and machine learning systems require vast amounts of sensitive patient data to function effectively. This raises significant concerns regarding data privacy and security, as healthcare organizations must protect against breaches that could expose confidential information. The complexity of managing personal health data is compounded by stringent regulations, making compliance a critical challenge for machine learning applications in healthcare.

Bias and inequity

Machine learning algorithms can perpetuate or even exacerbate existing biases if they are trained on skewed datasets. Biased data can lead to unequal treatment outcomes across different demographics, undermining the goal of equitable healthcare. Addressing bias in AI and machine learning requires careful attention to the data used for training and ongoing monitoring of AI systems in practice.

Automation Bias

Healthcare professionals may develop an over-reliance on AI and machine learning recommendations, leading to automation bias. This cognitive error can result in medical mistakes if clinicians fail to critically evaluate AI outputs or override them based on their expertise, potentially diminishing the benefits of machine learning in clinical decision-making.I

Integration with existing systems

Integrating AI and machine learning systems into existing healthcare workflows can be complex and resource-intensive. Many healthcare organizations face challenges in aligning new AI technologies with current systems and processes, which can hinder adoption and limit the effectiveness of machine learning applications in healthcare settings.

Ethical Concerns

Using AI and machine learning in healthcare raises various ethical dilemmas, including issues of accountability when errors occur, informed consent regarding data usage for machine learning models, and the potential loss of human empathy in patient care interactions. Establishing a robust ethical framework is essential for guiding the responsible use of AI and machine learning technologies in healthcare.

Implementation costs

Implementing AI and machine learning solutions can be costly due to the need for advanced infrastructure, software, and skilled personnel. Smaller healthcare organizations may find it particularly challenging to justify these investments without clear evidence of return on investment for machine learning-driven healthcare solutions.

Future of AI in healthcare

Now you know what big data and artificial intelligence look like currently and how they are helpful in modern medicine. What we should do now is to concentrate on the future.

What future of artificial intelligence and big data in healthcare is going to look like?

Artificial intelligence is producing great savings (it is estimated that by 2026, it will save up to $150 billion!). Its development will definitely go on. Firstly, we go back to the question from the beginning of this article – will machine doctors replace humans? It is probable. Actually, it happens already – for instance there is almost no need for human presence in radiology! Artificial algorithms are much more accurate in their judgments and above all – noticeably faster. When it comes to human life, time is the most important factor. Just seconds can change everything. So we expect to see a much bigger role of artificial intelligence in a diagnosis.

Another thing – AI in healthcare systems are “armed” with a lot of information so they can assist in clinical decision making. And their role in that part of medicine will go sky-high in no time. The minimization of diagnostic errors and therapeutic errors are the most obvious results. Future doctors will base their work and judgments almost entirely on artificial intelligence.

To sum up this part we might say that the future of artificial intelligence and big data in healthcare is full of great perspectives and fantastic potential!

Healthcare apps?

That happens already, but shortly they will be much more advanced and complicated. Imagine Google Assistant telling you it is time to do your blood tests. Or even doing these tests by itself through your smartwatch. Healthcare apps will be something more than they are now. They will act as a personal health assistant, keeping you updated about everything going on within your body. That will considerably shorten treatment time and lower its intensity. As it is well known that the faster you detect the disease, the easier it is to cure.

These are just a few examples of what the future of artificial intelligence in healthcare may look like. The physician of the future will only have to supervise the work done by the AI algorithms and robots. And maybe in a much longer time, there will be no need for a human physician? Just as it was in the Star Wars movies. Time will tell.

We hope that you enjoyed exploring the world of Artificial Intelligence and Big Data in Healthcare. We would love to hear your thoughts. Maybe there are some issues that you would like to learn more about?

Let us know!

This article is an updated version of the publication from Oct 11, 2019.