The future of artificial intelligence in healthcare

There have been many innovative developments in the healthcare industry since humans started practicing medicine. Yet only a few have had the impact of technology. Improvements in different aspects of digital technology, such as computers, programming and networking, have created a new part of healthcare and transformed how medical professionals perform their jobs.

While the impact of technology is felt strongly now, the idea has always existed as early as the mid-1900s. However, the high costs and unreliable performance of electronic devices made digital technology inadequate for medical practices. Today, with improved technology, the healthcare industry is starting to benefit significantly from digitalization.

Lots of technological applications in healthcare exist, from big data’s improvement of customer-centric services to electronic health record (EHR) systems. And now, another significant aspect of healthcare technology is artificial intelligence (AI).

The role of AI in medical practice has been a massive topic of discussion in the past couple of years, and no signs suggest that the adoption of AI in healthcare will slow down. In fact, the future of healthcare is deeply linked to this technology, such is its potential. In this article, we take a closer look at the future of AI in medical practice and training.

What is AI in healthcare?

In the context of healthcare, AI refers to the use of complex algorithms in automating complex medical tasks. AI systems receive medical data and, through these intricate algorithms, can review, understand and proffer solutions to complex medical problems.

Aside from sophisticated algorithms, AI also incorporates other cognitive technologies in a medical setting. Machine learning and natural language processing (NLP) have also been largely utilized in automating and streamlining various medical activities.

History of AI in healthcare

Like most digital technology, the concept of AI applications in healthcare is not new. The first application can be traced to the 1970s when AI systems helped with various biomedical issues. 

Following the success of MYCIN – a program that helped identify blood infection treatment – AI’s potential became evident, prompting broader research on the technology.

The following decades saw more sophisticated AI systems that helped achieve medical advancements such as:

  • Providing practical and quicker data collection and processing
  • Helping in precise surgical procedures
  • Full-scale implementation of EHRs

Since then, the development and adoption of AI systems has revolutionized the healthcare industry. Today, more research is being carried out to expand further the reach and capabilities of AI in healthcare. Based on the World Economic Forum annual meeting in 2020, we can expect to see the following applications of AI soon:

  • By 2030, AI will be able to access multiple data sources to reveal disease patterns and aid treatment and care.
  • AI-powered healthcare systems will be able to predict an individual’s risk of certain diseases and offer preventive measures.
  • AI will also reduce the waiting time for patients and improve how efficiently hospitals and health systems operate.

Categories of AI applications in healthcare

AI has impacted patients’ experiences and how medical professionals operate in healthcare. As AI finds its way into different aspects of medicine, its application can be categorized into three types: patient-oriented, clinician-oriented, and administrative-oriented.

Patient-oriented AI

For some time, increased priority has been placed on improving patient experience and engagement in a medical setting. As such, it’s unsurprising that AI has also been employed to serve this cause. In truth, no one knows you better than yourself, and through AI-enabled interfaces, patients now have more significant control over their treatment plans. Here are two examples of patient-oriented AI applications.

Virtual assistants 

Digital assistants can potentially improve the quality and delivery of healthcare to patients significantly. Using innovative technology such as cognitive computing, augmented reality and recognition software, a digital assistant is developed for patients to interact with.

The functions of this virtual presence cover different activities and are only limited by the algorithm’s complexity. Over time, we’ve seen virtual health assistants provide personalized experiences to patients and help individuals learn how to manage their health and illness better.

Medical bots 

The presence of interactive chatbots has become pretty popular in the healthcare industry. These bots are also a product of AI through NLP, sentiment analysis and concept extraction algorithms. These technologies are used to create an intelligent system that can respond accurately to questions relating to specific topics.

Medical chatbots not only help improve a patient’s experience, but also alleviate part of the burden on medical providers. The need for patients to be an active part of their treatment cannot be overstated. It helps improve health outcomes and satisfaction. However, it could become time-consuming and quite overwhelming to medical providers, and chatbots help solve this problem.

Employing these AI-powered tools ensures that patients achieve the best possible health outcomes without taking a toll on clinicians.

Clinician-oriented AI

This category directly covers AI solutions for doctors and other similar medical practitioners. Several AI applications are already in use for clinicians to help improve human performance on complex cognitive tasks. 

The decision-making process, for instance, is now more streamlined and effective with AI. Aside from acting as a decision support system, AI-powered systems can also make accurate medical-based decisions by analyzing data and utilizing algorithms to diagnose and create treatment schedules. Typically, clinician-oriented AI incorporates two significant applications.

Diagnostic imaging interpretation

Diagnostic imaging is a technology that allows doctors to look inside the body for signs of a medical condition. Engaging in diagnostic imaging requires a variety of machines and techniques to create a clear picture of the internal structure and activities inside the body.

Now, with deep learning programs and other categorization technology, AI-powered diagnostic imaging systems exist and are built with complex algorithms that foster quicker and more accurate imaging, including fluoroscopy, MRI scans, mammography, CT scans and X-rays.

Precision health

Precision health involves different approaches that everyone can take individually to protect their health. This new aspect of AI focuses solely on preventive care. This care is tailored for each individual based on patient data collected through genetic information, electronic health systems and intelligent devices.

By considering certain factors such as an individual’s environment and lifestyle, AI-powered systems can recognize possible health risks and suggest preventive actions. These two applications show how much AI helps ease the responsibilities of clinicians. As AI continues its upward trajectory, more applications will be developed and incorporated into different areas.

Administrative-oriented AI

Lastly, AI systems can also help automate and streamline various administrative processes. Automating specific operational processes improves efficiency by eradicating waste and limiting bottlenecks. Revenue cycle management and healthcare process optimization are two areas that have mainly benefited from administrative-oriented AI. Here are some applications of AI in streamlining administrative tasks.

Electronic-assisted grouping

Medical coding transforms healthcare diagnoses, procedures, medical services and equipment into universal medical alphanumeric codes. The essence of this grouping is to identify different aspects of healthcare quickly.

AI-powered systems built on NLP and machine learning technology can accurately suggest codes based on medical documentation. Medical coders can then use these recommendations to validate their codes for accuracy and efficiency.

Real-time documentation

Maintaining accurate records is vital in the healthcare industry. It allows clinicians to understand the patient’s history and to continue providing the best possible treatment for each individual.

Today, different clinics have specially designed, real-time, decision-making support systems that help improve how accurately, effectively and thoroughly a patient’s data is collected.

Live applications of AI

Aidoc/MaxQ – CT brain bleed diagnosis

In 2018, Aidoc, an AI healthcare provider, announced FDA clearance for its first AI-powered workflow solution that could diagnose bleeding in the brain. The system was developed alongside radiologists to effectively recognize acute brain bleeds in CT scans. This helps to alleviate the massive workload on radiologists.

The CT brain bleed diagnosis system can be seamlessly integrated into any medical setting and can be set up remotely. Its ability to streamline workflow and provide accurate diagnosis would prioritize this system for many healthcare institutions.

iCAD – breast density via mammography

In 2015, iCAD announced the iReveal system. This system is capable of monitoring breast density through mammography. A significant number of women typically have dense breast tissue that can block a mammography scan from identifying possible cancerous tissue.

With this system, patients who may experience a limited sensitivity to mammography due to dense breast tissue are identified. More accurate decisions can then be made regarding breast cancer screening. Today, this system remains at the forefront of breast cancer diagnosis in the US.

Neural Analytics – paramedic stroke diagnosis

Neural Analytics continued its tireless work in improving brain health by announcing its paramedic stroke diagnosis system in 2017. With this system, paramedics’ diagnosis of stroke victims has completely evolved.

Coined the Neural Analytics Lucid M1 Transcranial Doppler Ultrasound System, this AI-powered application solves the problem of costly and time-consuming stroke diagnosis for patients with blood flow disorders. It does this by effectively measuring cerebral blood flow velocities.

DeepMind – eye disease detection

This AI system developed by DeepMind, in partnership with London’s Moorfields Eye Hospital, has proven effective in spotting signs of eye disease as accurately as the best optometrists and ophthalmologists. This system functions on machine learning algorithms and incorporates data from over 15,000 patients, which allows the system to accurately identify different eye diseases.

There are many other real-world applications of AI in various aspects of healthcare. Mayo Clinic’s cervical cancer screening system, Viz’s CT stroke diagnosis system, and Icometrix’s MRI brain interpretation are just a few of the numerous AI-powered systems improving healthcare quality globally.

Benefits of AI in the healthcare industry

These applications show that AI systems are becoming more sophisticated at engaging in cognitive activities. These systems not only engage in these tasks accurately, but can also complete them quickly and at a lower cost. The potential for AI in the healthcare industry is extensive, and just like in our daily lives, it’s gradually becoming a critical part of the medical ecosystem. Below, we examine some of the benefits that AI has made in healthcare delivery.

Improving health

Perhaps the most significant benefit of AI is its impact on improving society’s overall health. Applications of AI and other sectors such as the Internet of Things (IoT) have helped people maintain good health without seeing a medical provider as much.

The most predominant implementation of this is in AI-powered applications that encourage healthier behavior in individuals. These applications also foster more proactive management of a healthy lifestyle. Individuals can have relevant knowledge of risk factors and control their health and wellbeing.

Aside from patient-centric services, AI also helps medical providers better understand patients’ daily patterns and needs. With this, clinicians can fashion more effective treatment plans, provide better feedback, and provide support for staying healthy.

End-of-life care

People who are close to the end of life require palliative care. This is because they tend to suffer from conditions such as osteoporosis, dementia and heart conditions. This phase of life is usually associated with depression and loneliness.

With AI, end-of-life care has been completely transformed through bots. Elderly people can remain independent for extended periods, thereby limiting the need for hospitalization and assisted living facilities. AI-powered robots can engage with aging people, performing various social interactions that help keep the mind sharp and active.

Early diagnosis of diseases

AI is already utilized to limit the impact of certain illnesses by detecting them early. Mammograms, for instance, have proven very inconsistent in identifying signs of cancer, leading to one in two healthy women being told that they have cancer. However, with AI, diagnostics imaging has improved. AI-enabled mammogram scans are about 30 times faster and offer up to 99% accuracy.

Additionally, the rapid increase of AI-powered medical devices and wearables has also been effective in identifying early-stage heart diseases. Medical providers can recognize potentially lethal heart conditions and treat them at the early treatable stage.

Accurate diagnosis

Different healthcare institutions recognize how effective cognitive technology is in exploring vast amounts of health data and diagnosis accuracy. Consider IBM’s Watson Health – a system that helps meet healthcare needs with cloud, data, analytics, and AI solutions. This system helps in documentation, making diagnosis and treatment much faster than before.

Another case study is Google’s DeepMind Health, which works closely with medical providers to address complex real-world problems. By combining the neuroscience discipline with machine learning, complex algorithms are built into neural networks to imitate the brain.


Today, AI has already become a part of healthcare courses, albeit in a very limited capacity. Professionals in training are able to go through real-life simulations and solve complex problems. Students benefit from NLP and the ability of AI systems to create a plethora of real-world scenarios.

Training is not yet a popular aspect of AI in the healthcare industry. Nonetheless, it’s one that holds a lot of potential in developing highly competent healthcare professionals. For now, professionals can engage in quality programs from qualified institutions such as Rockhurst University.

Rockhurst University offers a post master’s FNP online program for active registered nurses looking to grow their skills and opportunity by adding a second master’s degree as a family nurse practitioner (FNP). The FNP program offers greater flexibility, better salary, personal fulfillment, and better opportunities.

Through its benefits, it’s evident how much potential AI has in the healthcare industry. However, as with most technological developments, it also comes with some associated limitations. Some of the known concerns raised from AI’s adoption in healthcare include data privacy issues, compliance with regulations, stakeholders’ complexity, and initial adoption issues.

These factors continue to be significant roadblocks regarding AI adoption in healthcare. Nonetheless, it’s only a matter of time before AI is widely accepted and adopted in the medical ecosystem.


In the US, it takes an average of 12 years for an experimental drug to travel from the laboratory to your medicine cabinet. Additionally, the average cost of developing a new drug and providing it to patients is about $359m. These statistics show how long and expensive it is for patients to get new treatments. 

In line with this, AI has now been introduced into drug research and development. Utilizing effective AI technology in streamlining various research and development processes helps significantly reduce the time and cost associated with drug research, manufacture and marketing.

Effective treatment

Typically, health professionals acquire health records from patients. With these records, they can identify individuals who could potentially fall victim to severe episodes. Aside from diagnosis and early detection, AI also helps provide a more thorough outlook on disease management by creating more effective treatment plans and extended care programs.

Healthcare institutions also incorporate robots, ranging from essential virtual assistants to precise surgical treatments. Surgical robots, in particular, could function as an assistant or perform the entire operation independently. Aside from surgical procedures, AI-powered systems also help in other treatment processes, such as physical therapy and rehabilitation.