The Future of Artificial Intelligence in Healthcare

By: Lisa Morris on July 7, 2021

What is the future of artificial intelligence (AI) in healthcare?

It’s a big question that almost every medical professional has had cause to ask recently— especially smaller, independent practices that have to balance the need to offer cutting edge tech to their patients with budgetary concerns.

And the answer to the question is fairly elusive, regardless of who’s asking. In fact, at this moment, the answer is, “We don’t exactly know yet, but it’s going to be monumental.”

There is, of course, AI technology being developed and used today that we can look at to inform our prediction of how AI will be used in the healthcare industry tomorrow. By looking at what large healthcare organizations are doing with AI right now, we can predict what AI applications will be available (and even commonplace) among smaller practices in the future.

By the end of this article, we will have an answer to our question. And we’ll craft that answer using Gartner research to identify healthcare use cases for AI applications that are currently being used (content available to Gartner members).


AI solutions for doctors

AI solutions for patients

AI solutions for administrators

How to overcome barriers and prepare for the

future of AI in healthcare

AI solutions for doctors

For providers, a number of AI applications are already being used to help “enhance human performance on cognitive tasks.”

For example, AI is helping providers with decision support, or even making decisions itself, by analyzing data and applying machine learning and/or algorithms to diagnostics and treatment plans.

Two existing applications that fall into this category are:

Diagnostic imaging interpretation: Using deep learning programs and categorization technology, AI-enabled medical imaging systems are now equipped with algorithms for faster and more accurate image reading, including X-rays, MRI exams, and CT scans.

Precision health: This is a relatively new AI solution focused on preventative care, and based on patient data collected from genetic information, wearable devices, and advanced electronic health systems. Using information such as a patient’s lifestyle, environment, and biometric healthcare data, AI-enabled precision health tools can identify potential risks and suggest preventative interventions.

As these two examples illustrate, artificial intelligence is currently making life easier for medical providers, and it will continue to do so as these applications are further developed and iterated into new areas.

With this knowledge, we can begin crafting our answer to the original question:

The future of artificial intelligence in healthcare is

better medical outcomes achieved through more accurate diagnosis and advanced treatment.

AI solutions for patients

With a growing focus on patient experience and engagement in healthcare, it’s natural that AI has been adapted to serve patients as well as providers. Through the use of AI-enabled interfaces, patients now have more power and control over their own care paths.

Two examples of patient solutions using artificial intelligence are:

Virtual health assistants: Using augmented reality, cognitive computing, and speech and body recognition software, a virtual persona is created for patients to engage with. These virtual health assistants are able to provide a personalized experience in which patients can ask questions and learn how to better manage their health.

Healthcare bots for customer service: This AI application is an interactive chatbot that uses natural language processing (NLP), sentiment analysis, and concept extraction algorithms to respond to patient statements and questions around administrative things such as bill payment, appointment scheduling, or medication refills.

We know that patients do a better job of managing their own chronic conditions and that outcomes improve when they are engaged with their care, but staying in regular communication with individual patients adds a time-consuming burden to already overwhelmed medical providers.

With these AI tools, patients can achieve positive results while providers are granted more time to focus on other tasks. Thus, we can expand our answer to what the future of AI will be:

The future of artificial intelligence in healthcare is

better medical outcomes achieved through more accurate diagnosis and advanced treatment, as well as more informed and engaged patients empowered with easy access to healthcare support.

AI solutions for administrators

Finally, AI algorithms can go a long way towards automating administrative processes, eliminating waste, and reducing bottlenecks by better managing all of the data that a medical practice produces.

Currently, AI is being used to great effect in the realm of revenue cycle management and optimization. Two examples of that are:

Real-time physician documentation improvement: Using specialized, real-time decision support models at the point of care, physicians can increase the accuracy, comprehensiveness, and efficiency of their note taking to collect more valuable patient data.

Computer-assisted coding: Programs using NLP and machine learning can suggest medical codes based on clinical documentation. Coders can use these suggestions to amend or validate their codes and ensure the highest reimbursement rate possible.

And with that, we’re able to account for administrative tasks in our answer:

The future of artificial intelligence in healthcare is

better medical outcomes achieved through more accurate diagnosis and advanced treatment, as well as more informed and engaged patients empowered with easy access to healthcare support, and more efficient administrative processes using automated documentation and coding algorithms.

How to overcome barriers and prepare for the future of AI in healthcare

According to Gartner, “The AI/smart machine era will be the most disruptive in the history of IT… eventually, these advances will redefine what it means to be a physician and a patient.” (Content available to Gartner members.)

However, as these advances are made, Gartner has also identified current challenges to adopting AI. The most notable one is a lack of trust in AI-based technologies from medical providers.

Those medical providers who fail to recognize the benefits of AI are in for a wake-up call.

As Gartner puts it, “Physicians must begin to trust the use of AI so they are comfortable using it to augment their clinical decision-making. There is simply so much information available to use when making medical and diagnostic decisions that it is truly beyond the cognitive capabilities of the human brain to process it all.”

What’s more, we know from a recent Software Advice survey that patients themselves have a great deal of trust in artificial intelligence applications for healthcare.


AI has been and is being tested on a large scale by massive healthcare delivery organizations, and the beauty of operating an independent medical practice is that you get to watch these giant providers as they test out technology before it becomes ubiquitous (and before patients start expecting to see that tech in smaller practices).

When it comes to incorporating AI into your small practice right now, there are a few options. For example, you could consider adopting an EHR with AI-enabled features or incorporating an AI-powered chatbot into your practice website or telehealth platform.

For the bigger, more expensive AI applications, though, small practices are better off watching and waiting. Keep an eye on the large-scale providers as they incorporate AI-powered technology more and more, and use their examples to figure out what applications will be the most beneficial to you in the future.

Want to learn more about AI in healthcare? Check out these articles:

Artificial Intelligence is Transforming IT for

Small Medical Practices

Understanding Precision Medicine and Its


Are Patients Ready for Amazon Alexa, MD?


To collect the data presented in this report, we surveyed 1,296 patients within the U.S. in May 2021. We used screening questions to narrow the respondents down to 1,001 with the relevant and timely experience needed to provide accurate answers to these survey questions.