Written by Alexandr Morozov, founder of the Laboratory of digital products Geerafe.

When ChatGPT became the fastest growing web-app in the history of digital entrepreneurship, it made one thing very clear — we’re entering the age of artificial intelligence. But this technology has applications that reach far beyond smart chatbots — such as healthcare Machine Learning.

Right now, we’re seeing a huge surge in the use of AI and ML (Machine Learning) technologies in healthcare and medicine. In fact, the worldwide market for AI in healthcare is expected to grow at a yearly rate of 37.5%, starting from a value of USD 15.4 billion in 2022.

We wouldn’t be seeing this kind of growth if it weren’t for the tangible benefits AI brings to patients and healthcare providers.

In this article, we’re going to talk about six major advantages of using AI in healthcare and what they mean for both patients and those giving care.

1. Improved Diagnostic and Medical Trial Accuracy

Machine Learning models can accurately analyze large datasets, including medical images, lab results, and electronic health records, spotting patterns that might escape the human eye.

For example, clinical trials sometimes fail because a group that responds well to medication is overlooked during evaluation. AI can help find these outliers, no matter how much data it needs to process.

This same ability to handle a lot of information gives AI the potential to boost the diagnostic accuracy for common conditions such as heart diseases, and rare genetic disorders.

 

2. Predictive Analytics for Early Intervention

AI can enable early intervention by analyzing patient data and identifying individuals at high risk of developing certain diseases. Predictive systems like this are already deployed today with great success.

For example, Google’s DeepMind helps detect eye diseases by analyzing retinal scans, which enables early treatment and potentially saves vision. 

Predictive systems utilize machine learning algorithms, trained on extensive health data. That’s why they can detect abnormalities even if the symptoms are easy to overlook.

 

3. Optimization of Administrative Tasks

Behind every healthcare facility there is a complex system of appointments, billings, and logistics.

These administrative tasks can often bog down medical staff, diverting their focus from patient care. This is where AI can step in.

For instance, this technology can automate appointments and predict optimal time slots based on historical data and real-time availability. This will streamline the scheduling process and free up valuable time for hospital personnel.

 

4. Remote Monitoring and Telemedicine

The COVID-19 pandemic has underscored the importance of remote care. As a result, telemedicine adoption has skyrocketed in many parts of the world. In the Asia-Pacific region, for example, adoption rates nearly doubled between 2019 and 2021.

As telemedicine continues to become more popular, doctors need to adapt to this new way of working. For the telehealth physicians, who understandably can’t physically poke and probe a patient, the ability to make accurate, data-driven decisions is key.

AI is very effective when it comes to analyzing large datasets, and it is only natural that in telehealth many healthcare providers use it for data analysis and diagnostic assistance. And, according to a study by MIT, 75% of respondents said artificial intelligence helped them provide better care.

 

5. Efficient Workload Management

Working in a medical facility is highly demanding. Physicians and nurses, who put in lengthy hours engaged in physically taxing tasks, find themselves particularly prone to fatigue. This, in turn, can potentially compromise their judgment in critical situations.

AI can be used to keep tabs on work hours and suggest rest periods to prevent overworking. A system like this can solve the fatigue problem in an industry where schedules aren’t yet regulated.

 

6. Personalized Treatment Plans

Personalized medicine, or precision medicine as it is also known, sorts patients into groups. It looks at each person’s unique traits, especially differences in their genomes.

In simple terms, doctors can use this approach to guess how each patient might react to a certain disease, and then recommend treatments just for them. This is a really promising area in medicine, especially when dealing with tricky genetic conditions. But, the cost issue often slows down progress.

That’s where artificial intelligence  can make a big difference. It can make it cheaper to analyze important data, like a patient’s medical history and genomic information. If we use this technology right, precision medicine could become a go-to way to treat patients.

 

But it’s not all smooth sailing

Even with all the good things AI can do for healthcare, it’s not an easy road to get everyone using it — we’ve got quite a few challenges to face before we can outstaff hospital work to robots. Here are some of the biggest hurdles:

 

Not all patients are ready to embrace AI

One of the most significant challenges is the public’s perception of AI in healthcare. A survey showed that 60% of Americans would be uncomfortable with their healthcare provider relying heavily on AI.

This unease comes from a few places, like not really understanding what AI is, and worries about privacy and keeping their data safe.

 

We don’t know how LLMs work

Another big issue is that we don’t really know how Large Language Models (LLMs) work. Using these systems without fully understanding how they make their decisions can be risky and bring up some serious ethical questions.

For instance, LLMs like the technology behind ChatGPT use predictive algorithms to try to guess the best answer. The thing is, we don’t really know how they come up with those guesses. What’s more, we don’t have a good way to study it.

If we looked at an LLM’s code while it’s working, all we’d see is arrays of numbers flipping around — kind of like that scrolling code from The Matrix movies. The weird part is that even the people who made the program can’t make sense of that data.

So, this brings up a tough question: can we really trust a tool we don’t fully understand, especially in something as critical as healthcare? And what happens if an AI makes a mistake that costs a life?

 

Wrapping up

To wrap things up, even though AI’s part in healthcare is still changing, there’s no denying it has huge potential. Yes, there are challenges, but the benefits it brings — like making diagnoses more accurate, customizing treatment plans, and managing work better — are just too big to ignore.

As we continue to navigate the future of healthcare, AI will undoubtedly play a pivotal role, changing the way we diagnose, treat, and prevent diseases.