From the beginning, humans have expressed a hunger for innovation, and our minds are constantly seeking to grasp new concepts, ideas, and milestones just out of reach. The level of acceleration in different fields and industries has been fueled by this hunger, and AI is a prominent result of it.

Artificial Intelligence, or AI, is simply a combination of different technologies that mimic our own cognitive functions. AI provides computers with the ability to learn and apply logic, which can prove to be consequential in bringing about positive change in the healthcare sector.

How is AI Changing Healthcare?

The healthcare industry has been ripe for technological advancement for quite some time. With the help of AI, healthcare professionals—scientists, researchers, and doctors—can process significant amounts of data in order to make their systems more reliable and efficient.

According to analysis conducted by Accenture, a combination of AI and healthcare can potentially create an annual savings of $150 billion. This reduction in treatment costs and the creation of an additional layer of support for doctors and physicians lead us to believe that AI will be a game-changer for the industry.

In addition to reducing treatment costs, AI can also help create the next generation of radiology tools. These tools can produce better imagery of human tissues, which can then be used to study diseases and get new information to achieve a proper understanding of the cause and effect of these diseases.

If researchers succeed in this quest, it can also mean success in the life-long challenge of developing an understanding of how tumors behave in the human body. It could ultimately lead to treat cancers more aggressively and find out a treatment method that is more appropriate and has a better success rate.

All of the changes we've listed so far are pretty general, but what are some specific instances that highlight how AI can transform the healthcare sector and the challenges they may bring?

A woman in the middle of a virtual appointment with her doctor via laptop.
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1. Expanding Access to Remote and Developing Areas with Virtual Health Assistance

Nearly half of the world’s population currently lives in remote areas, and the majority of healthcare workers reside in cities. This imbalance creates a lot of challenges to provide worldwide healthcare services and has a severe impact on the quality of care available.

Artificial Intelligence can be used to mitigate these impacts by taking over some basic diagnostics duties from healthcare providers. A portable diagnostic station can be introduced in these areas to conduct basic tests like blood pressure, electrocardiographs, and routine blood and urine analysis.

In addition to this, advanced AI technology can also be used to reduce the time and cost barriers of quality healthcare so that it is more readily available to everyone, regardless of where they live or how much money they make.

The Challenge: Infrastructural Issues in Rural Areas

With that being said, rural areas may lack the kind of infrastructure needed to support virtual care. If electricity or internet is an issue, getting these systems in place first will be instrumental to its success. There are organizations working to help more rural communities gain access to broadband internet, and it may be worthwhile to reach out to them and see if they can help.

An up-close image of an EKG reading.
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2. Merging Mind and Machine by Introducing Digital Interface

The idea of human-machine communication is not a new concept as we have been doing it for quite some time now, but creating a digital interface that can be operated without the help of any input device like a keyboard or a mouse often proves to be a challenging task.

Artificial Intelligence has laid some groundwork into the field as brain-computer interfaces are being developed to assist in establishing faster and more efficient communication methods. Once computer devices develop a level of intelligence comparable to that of humans, they can be used to treat different neurological diseases and trauma to the nervous system.

The Challenge: Compliance with Healthcare Standards

A recent study states that Brain-Computer Interfaces, or BCIs for short, have a promising future as the market is now worth more than $1 billion globally. There is still a long way to go for clinical trials, however, and regulatory approvals will be crucial to their success. This is going to take a lot of patience and willingness from the investors, but the benefits greatly outweigh any risks.

A person being put inside of a CT scan machine.
Image by Bokskapet from Pixabay

3. Precise Medical Imaging Diagnosis through Intelligent Scanning Machines

One of the most promising areas of healthcare innovation is the use of AI in scanning machines. Common imaging tests like x-rays could be upgraded to perform automatic detection of any abnormalities in the body so that a quicker diagnosis can be performed.

Another area AI can revolutionize is the screening for common cancers. Currently, radiologists have a hard time conclusively identifying microcalcification in tissues that would help diagnose cancer in a patient, and AI can be used to improve the accuracy of these tests so that there are fewer errors.

The Challenge: Training of AI Systems

The main hurdle here in the implementation of AI in medical imaging diagnosis machines is the unavailability of data and the challenges in accessing large data sets. In order to make machines learn these tasks, several algorithms have to be devised that require validation from data sets, which can be challenging to come by.

4. Bringing Efficiency to Electronic Health Record

Data is one of the most important assets available to hospital administrators and analysts when it comes to improving healthcare operations and care. Initially introduced as a digital health tool to save patient information, Electronic Health Records (EHRs) have proven to be even more useful as a method for extracting data for both internal and external registries. Thanks to EHRs, we have more information to create statistics for various illnesses and disorders, which help with important research and development.

EHR developers can now implement AI to create better interfaces and automate most of the tasks within the system so that administrators and analysts can spend their time on other tasks while everything runs efficiently in the background. Voice recognition and natural language processing can also be used to improve the clinical documentation process so that routine tasks can be performed on a priority basis.

The Challenge: Data Security and Database Integration

The main challenge for this task is figuring out the best way to implement AI in the current EHR system. As the current database architecture is decades old, the system will require a bit of tweaking and trial and error to get it right. Ultimately, it may take quite a bit of time, but it certainly is possible!

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5. Better Health Monitoring With the Help of Wearable Technology

Wearable tech has become increasingly popular in many different parts of the world because it provides us with the option to monitor our fitness levels, read text messages, and track our location with GPS all without needing to take our device out of our pocket!

Machine learning algorithms can be utilized in wearable heart monitors and mobile phone apps to monitor a patient’s condition remotely. AI wearables for the blind and visually impaired are being developed that can help them safely navigate through their surroundings by steering them clear of any obstacles. Using haptic vibrations, this tech provides feedback to its users and detects objects through sonar and echolocation.

The Challenge: Testing and Training

Despite its initial success, there is still a lot of room for growth and innovation, and this can easily be accomplished with the power of machine learning and artificial intelligence. There is still a lot of groundwork and testing to be done, which takes time, but these devices have an immense amount of potential. Fortunately, some of the greatest minds in the world are working on it, which gives us a lot of hope!

A smart watch that shows heart rate and how many steps the wearer has walked.
Image by Artur Łuczka from Pixabay

6. Powering Predictive Analytics with AI-Powered Tools

As humans, we're limited in what we can do with the data available to us when it comes to time, energy, and resources. Healthcare professionals are not immune to this. With the assistance of AI's advanced data processing capabilities, healthcare providers are able to produce massive amounts of prediction-driven data within seconds, and that data can be used to determine individual outcomes like what someone's likelihood of contracting a disease is, predicting infections, calculating wellness down the road, and so much more.

These advances in AI technology are not only leading to an improvement in treatment quality, they're drastically reducing treatment costs as well.

The Challenge: Privacy

Predictive analysis provides healthcare providers with a head start whenever a new patient walks into their office. They can use EHR to learn more about their patient's medical history and then base their diagnosis in part on predictive analytics in order to minimize the number of tests required. The only limitation here really is privacy. In order to utilize this technology to its full extent, you'd require massive amounts of data, and in many cases, that data can be highly sensitive and private. Patients may not be comfortable with signing away such highly personal information.

AI Adoption in Healthcare Industry: Operational Benefits

Personalized Medication

AI aims to shift the current medical treatment trend of one-size-fits-all approach in lieu of a more personalized approach. With the help of AI, pharmaceutical companies can collect large sets of data to carry out research, and that data set can then be used to determine how a specific group of patients will react to a certain medication for an easier treatment plan.

A table side view of a surgical procedure involving four medical professionals.
Image by Sasin Tipchai from Pixabay

Advanced Treatment Plans

Various AI-driven techniques can be used to overhaul a lot of current treatment processes. On top of being used as a tool to moderate diagnostics, drug development, treatment personalization, and gene editing, AI can also be used to introduce robotic assistance so that surgical operations can be performed with better accuracy.

Streamlining Processes

Since the use of AI means that there is less human intervention, it enables us to develop a system without the danger of human error. Industry experts believe that the full integration of AI is going to transform how healthcare is practiced as it is going to improve lots of decision-making and surgical processes for doctors and physicians.


There is no denying that machine learning and artificial intelligence have many innovative use cases in the healthcare industry. If you're looking to implement these solutions for your business in the healthcare field, it's important to work with a trusted partner that understands healthcare technology and can provide you with the solutions you need efficiently.

Crowdbotics provides managed app development to help healthcare providers build patient-facing, vendor-specific, or administrative tools. Our custom software solutions for hospitals and independent physicians can improve operations, simplify billing, and deliver better patient outcomes. If you are considering a custom solution to increase your hospital's efficiency and improve patient outcomes, get in touch with one of our experts today for a detailed quote.