How to Enhance Your Healthcare Services with AI and Machine Learning

The growing interest in artificial intelligence and machine learning is more than just a passing hype. The popularity of ChatGPT, Midjourney, Tableau, and other tools clearly shows that AI-powered projects are here to stay.

Read also: GPT-4 vs. GPT-3: Which Model to Use for Your Business

The numbers support that. According to IBM’s Global AI Adoption Index, in 2022 35% of companies were using AI in their business, with an additional 42% exploring it. Nearly half of the businesses state that using AI benefits them in a variety of ways: allows them to save costs and increase efficiency (54%), improve network or IT performance (53%), as well as offer customers a better experience (48%).

AI adoption rates around the world, according to IBM

The implementation of AI and machine learning can benefit companies from different industries, healthcare included. In fact, in 2021 the AI in healthcare market worth reached nearly $11 billion, with this number forecasted to increase to $187.95 billion by 2030. To top this up, Accenture claims that AI usage can potentially save $150 billion annually for the US healthcare economy by 2026.

Read also: Artificial Intelligence in Medical Billing Systems

How exactly can you use AI and machine learning in healthcare solutions and benefit from it? Mind Studios has some suggestions. With vast experience in the software development market and many successful cases in the healthcare niche in particular, we know how AI solutions can solve existing problems of the niche and will gladly share our knowledge and insights with you.

Benefits of AI and Machine Learning in Healthcare

Benefits of AI and Machine Learning in Healthcare

No matter how valuable a technology might seem, it could be difficult to understand its true value for a certain industry without outlining specific and niche-related benefits of AI in healthcare. So here’s how using AI and ML in healthcare can aid your business.

Enhanced patient care and experience

Healthcare environments are typically associated with lots of confusion. This could add to the potential anxiety and other difficult feelings that patients and their close ones experience while attending a medical institution. The overall mix of emotions, crowd, and chaos could be extremely upsetting.

The 2020 KLAS study supports that, outlining that 83% of the respondents name poor communication the worst part of their patient experience.

One of the main perks of AI- and ML-powered tools is their ability to analyze and structure large chunks of data fast. In healthcare, such ability can be used to simplify the processes in medical institutions, quickly find reports and health records, and redirect patients to appropriate specialists. As a result, patients can receive treatment quicker, avoid chaos, and feel more comfortable.

Machine learning in healthcare can be used to design virtual assistants and conversational AI for healthcare that give all the necessary information to patients. Unlike phone and on-site support, such tools are available 24/7 for no extra cost, making communication even more convenient. They can also help more patients without compromising the quality of support.

AI and ML can also generate personalized treatment plans based on patient health data and medical history and adjust them while monitoring the progress.

Read also: Benefits of Integrating AI into Healthcare Software Solutions

Increased efficiency and productivity

Modern healthcare institutions have complex policies and deeply interconnected processes, which sometimes can be overwhelming both for patients and for administration. Luckily, AI and ML can make it all much simpler, resulting in increased efficiency and productivity.

Task automation is one of the quickest ways to achieve that. Artificial intelligence technology in healthcare can be used to automate such routine tasks as data entry and appointment scheduling. It can also manage and structure electronic health records, as well as process insurance claims, and prioritize services based on patient needs and available resources.

Where there is bustle, there are always increased chances of various mistakes caused by the human factor. However, when such processes are handled by AI and ML, this doesn’t only make it easier for administration but also reduces the risk of potential errors.

Improved diagnosis and treatment

In the US, 7.4 million diagnosis errors are made every year. Nearly 6% of the patients who turn to healthcare specialists receive the wrong diagnosis. The consequences of that can vary from insignificant to dreadful.

As we already mentioned, AI and ML can quickly process and analyze large chunks of data, including medical history, test results, and symptoms, and, as a result, come up with diagnosis suggestions. Furthermore, the same technology can be used to identify certain patterns in patient information that can indicate disease development. Doing so makes it possible to spot diseases in their early stage or prevent them entirely.

Some might worry if diagnosis and treatment suggestions made by such algorithms are accurate enough. True, AI algorithms have to be trained first, using enough high-quality data coming from diverse backgrounds. Doctors and other healthcare professionals also have to be trained to work with AI to recognize potential mistakes.

However, even despite that using AI and machine learning for healthcare leads to impressive results. According to a 2022 study conducted by researchers from University College London and Babylon Health, the new AI model scored higher than 72% of general practitioner doctors when it came to diagnosing.

Better data analysis and insights

Using AI and ML in healthcare processes can help save both time and resources required for patient examination and diagnosis. Medical personnel will be able to act faster and make critical decisions quicker and with more accuracy, saving more lives as a result.

The ability to analyze large amounts of clinical data also allows AI algorithms to offer a more holistic view of the health status of patients, therefore improving care outcomes. Imagine a tool that can process all the records in a medical institution, identify patterns, and offer conclusions and potential solutions. Sounds good, right?

Use Cases for AI and Machine Learning in Healthcare

AI and ML can not only potentially improve healthcare products — they are already doing so. The following artificial intelligence use cases in healthcare are a great example of that.

Medical imaging and diagnostics

Use Cases for AI and ML in Healthcare: Medical imaging and diagnostics

Medical examinations lead to the creation of huge amounts of various data, including graphical ones. Cardiograms, CT and MRI visuals, ultrasound results, and other things have to be analyzed daily for more precise diagnostics.

However, it’s difficult to argue that this process requires lots of time and effort. Using AI in medical imaging can help avoid that. Algorithms analyze data and compare it with other studies to trace patterns and offer suggestions.

As a result, this:

  • minimizes the risk of mistakes;
  • makes the diagnostics more accurate;
  • allows healthcare professionals to quickly identify severe cases and important factors for their treatment.

For instance, the Behold AI solution is used to analyze CT and X-Rays to spot various conditions, such as stroke and lung cancer. The platform delivers the result almost instantly, is CE and FDA approved, and is CQC Registered.

Patient behavior adjustment

Use Cases for AI and ML in Healthcare: Patient behavior adjustment

As far as we know, many serious diseases, such as obesity and type 2 diabetes can be avoided completely if a patient leads a healthier lifestyle. However, sometimes it’s easier to say that than to actually do. Changing one’s habits is a long process that requires additional assistance.

Artificial intelligence healthcare applications can be used for that. Most people trying to build a healthier lifestyle use various apps, such as calorie trackers and apps for holistic healing. While such apps often prove to be efficient, some users still need tools that are more advanced and adjusted to their personal behavior patterns.

AI- and ML-powered apps can use personal medical records and data collected from health devices such as AI wearable devices to understand patients’ behavior better and guide them on their way to a healthier lifestyle.

Somatix is one of the most diverse machine learning in healthcare examples for that use case. The platform collects medical data to gain valuable insights and provide various types of solutions. For instance, one can use it to give up smoking, get tips for healthier senior living, and initiate substance or drug abuse recovery.

Drug discovery and development

Use Cases for AI and ML in Healthcare: Drug discovery and development

Another important role of artificial intelligence in healthcare is to help discover drugs quicker and at a lower cost. As of now, the process is lengthy and costly: to identify one viable drug, you might need to test thousands of elements.

But when AI and ML technologies enter the scene, all of this becomes more simple. ML algorithms can use previously collected data on drugs’ active components and the way they influence the human body. Then you can model an active component that can treat a similar disease. Such algorithms can also predict how drugs will perform potentially and find new ways of usage for substances that have been previously tested.

For instance, Owkin company uses both AI and human knowledge to understand disease patterns and discover novel drugs and their combinations.

Electronic Health Record (EHR) management

Use Cases for AI and ML in Healthcare: Electronic Health Record (EHR) management

When it comes to EHR management, AI and ML can help with the following things:

  • allow both patients and healthcare specialists to obtain all the necessary data quickly;
  • ensure regular updates of the patient records;
  • scan data to offer diagnostics support;
  • offer personalized treatment suggestions based on a patient’s EHR.

For instance, Nuance is one of the best examples of artificial intelligence in healthcare for EHR processing. Its AI-based tools can integrate with commercial EHRs to collect data and compose more accurate clinical notes.

Best Practices for Implementing AI and Machine Learning in Healthcare Services

Best Practices for Implementing AI and Machine Learning in Healthcare Services

There’s no one-size-fits-all approach when it comes to the integration of AI and ML algorithms into healthcare solutions. Due to the variety of AI-powered tools and user-centered healthcare software types, each case should be viewed individually. After all, using AI for a mental health app and for a hospital will be different.

Still, there are some general rules you can follow to succeed in creating artificial intelligence applications in healthcare.

Remember that AI is a tool

AI and ML can help you improve certain processes but this doesn’t make them the main solution for your project. You need to understand your priority problem and the type of app you want to build to pick the right AI algorithm for your needs.

For instance, many tools that use machine learning (like ChatGPT) often store the data on their servers. While this helps them learn and improve, this doesn’t meet the HIPAA (Health Insurance Portability and Accountability Act) compliance rules. Respectively, this makes such tools impossible to use in medical software that has to meet HIPAA requirements.

Read also: How to develop a HIPAA-compliant healthcare software

Have accountability

Despite AI and ML algorithms being already efficient and quite advanced, there are still no specific regulations regarding them. This reminds the internet in its early days, when people could share data without any restrictions and didn’t have to comply with laws simply because there weren’t any.

The same happens with AI right now. Although the first regulatory proposals are already appearing, at the moment there are no clear legislations. Still, this makes the application of ethical principles even more crucial.

Healthcare solutions using AI and ML have to follow the existing guidelines and regulatory oversights to ensure that no harm is done to patients. For instance, you might need to check if the AI tools you decide to use are trained using data from systems with the same patient mix. Otherwise, they might not work well for your patient base specifically.

Make sure that your target audience needs and understands the tools

First, AI- and ML-powered tools have to actually solve critical problems for your target audience. Trying to implement decision-making algorithms in areas of medicine that don’t have any precise solutions now or might not have them at all won’t work well.

For instance, at the moment there is no standard psoriasis treatment that helps all patients. While there are some general recommendations, they still work individually for each person. Therefore, if AI decides to introduce one treatment algorithm for this disease, most likely it won’t be efficient for all patients and won’t help the doctors.

Second, healthcare professionals need to understand how AI-generated recommendations work and what lies behind them. This is important because this allows the specialists to analyze such predictions, spot flaws, and discard them if necessary.

For instance, some AI tools learn only from the data of served patients. Not taking the information from underserved ones into account might affect the results.

Take the costs into account

The budget for the AI and ML implementation depends not only on the cost of tools themselves but on many other factors, such as data system integration, user training, result tracking, testing, and other things. While some of such tools also help decrease future costs, some could also decrease revenue or simply turn out to be too expensive for your budget.

One of the most common ways to cut costs on AI and ML implementation is to use trained modules and tweak them to your needs instead of training them yourself. This helps save on purchasing or renting powerful hardware required for training.

Risks and Challenges of AI and Machine Learning in Healthcare Services

While the benefits of artificial intelligence in healthcare are significant, we still have to take possible risks into account. And just like any other technology, AI and ML have their own challenges.

Data privacy and security

We’ve already briefly mentioned that the ways to store the data in some of the AI products don’t meet HIPAA compliance requirements, and also talked about how to develop HIPAA compliant software. But even if you don’t need your tool to comply with this regulation, you still have to keep patients’ privacy in mind. If the data used to support AI tools is stored on servers with weak protection levels, it increases the risks of unauthorized access and breaches, and, as a result, endangers patients’ sensitive data.

Lack of skilled professionals

As AI and ML technologies are relatively new, there currently aren’t many experts in the niche — and the ones who are considered experts are in very high demand. The shortage of developers with such a background often encourages businesses to search for the right talents in other countries.

Human factor

Some patients and healthcare specialists are having second thoughts about AI and ML solutions taking more and more space in the industry. The reasons for that can vary: some patients don’t find such close interactions with machines comfortable, while some doctors fear that this will reduce face-to-face time, resulting in loss of personal approach and increased patient anxiety.

Mind Studios' Experience

The healthcare industry is one of our main areas of expertise. Even before the advancement of AI and ML technologies, we’ve been searching for ways to optimize the doctor-patient interaction while maintaining a high level of data security.

For instance, one of our HIPAA-compliant projects is a platform used to collect patient data for medical appointments. When a patient sees a doctor for the first time or with a new problem, it takes some time to gather all the information required for diagnosis.

Mind Studios’ solution allows us to simplify that. The doctors register on our platform using Epic and set up a form containing all the necessary questions required for primary diagnosis. When a patient sets up an appointment with a doctor, they get redirected to this form and have to fill it in.

Due to that, a doctor receives all the necessary data even before seeing a patient. As a result, they can generate possible treatment suggestions even before an appointment occurs. Furthermore, using Epic (a HIPAA-compliant platform) also makes our solution instantly HIPAA-compliant too.

As you can see, we are skilled in finding reliable and cost-savvy solutions for healthcare projects. We can both design an AI- and ML-powered app for you from scratch or help with the integration of these technologies into the existing app.


Integrating AI and ML technologies into healthcare projects can help the industry in so many ways. Such solutions allow automating routine tasks, optimize data processing and structuring, improve diagnostics, speed up drug discovery, and many more.

At the moment, AI- and ML-powered tools have their own challenges, such as data privacy and security concerns, lack of experts in the niche, and mixed reception from both doctors and patients. However, these problems don’t outweigh the benefits and can potentially be solved with time and as these tools develop.

If you want to add such technologies to your projects or build an AI-powered healthcare app from scratch, you have to remember that there is no general approach to that. Each solution depends on your needs, goals, and budget.

Mind Studios are always here to help you overcome possible confusion and set up a clear development plan for your tool. Get in touch with us for a free consultation that will help you figure out the technologies, the budget, and the timeline for your project.