Artificial Intelligence: Types, Potential and Implications for the Healthcare Industry

Artificial Intelligence (AI) and related forms of technology have the capacity to revolutionize the medical world, making patient care and medical administration easier and eliminating errors. Several types of healthcare Artificial Intelligence are already in use by providers of care, payers, and life sciences companies, but future use of these technologies will depend on their implementation.

Some studies imply that AI can outperform humans at vital tasks in the medical field, such as diagnostics and precision surgeries and procedures. In this article, we’ll look at all the ways that AI has affected the medical field, types of AI relevant to medicine, and the future of this technology to healthcare professionals everywhere.

The Future of AI in Healthcare

COVID-19 has accelerated the use of AI to deliver better healthcare services. The first four months of 2021 saw a huge increase in funding towards AI in medical services, with startups raising close to $2.5 billion. The progress may be slow as the AI technology hasn’t yet reached the experimental phase, and most technology depends on data collection, which is still insufficient at the moment.

The next few decades will most likely focus on studying the human aspects of healthcare and collecting data on human behavior. This stage is critical to ensuring that AI applications in healthcare are more human-centric than machine-centered. Most experts agree that the success of AI-based medical solutions depends heavily on understanding both patients and healthcare service providers.

The standards that govern the quality of data applied to AI development in healthcare are still ambiguous at this stage. The next step to merge artificial intelligence and healthcare will be to analyze all the cracks and holes in data collection and scrutinize the data accordingly.

Connected Hospital & Healthcare Facilities

Types of AI of relevance to healthcare

Artificial intelligence is a broad field with lots of branches. Here are some types of AI relevant to the medical field:

Physical robots

Robots are some of the most well-known applications of modern-day technology. Each year, more than 20,000 robots are installed in industrial setups. They are pre-programmed to perform common tasks such as lifting heavy products, transport, assembly, repositioning, and delivery. In medical AI, robots are used to deliver medical supplies and have recently been involved in surgical procedures. They have become more intelligent by embedding AI capabilities into their operating systems. Surgical robots were approved in the US in the year 2000. Gynecology, head and neck surgery, and prostate surgery are some of the fields that have greatly benefited from robotic advances. AI-empowered robots give surgeons the ability to perform minimally invasive and precise surgical procedures, such as suturing and incisions. Robots are still being governed by surgeons in the operating room since major decisions remain in the hands of experienced medical professionals.

Robotic process automation

This technology applies to the administrative part of medical services. It automates digital procedures that are crucial to the administrative process in a manner that mimics a human being following a set of guidelines. Despite its name, robotic process automation, or RPA, doesn’t involve robots at all.

Instead, it involves computer programs that depend on rules, workflow, and integration with information systems. This is the most cost-friendly type of AI. Its programming has a simple learning curve, and its workflow can be easily monitored. Robotic process automation is used for repetitive tasks such as billing and updating patient records. It can be enhanced using other AI technologies such as image recognition to extract and analyze data from images and photographs.

Rule-based expert systems

These systems were used in the 1980s, running on a set of rules drawn out by a team of experts or rule engineers. In healthcare, they are mostly used in making clinical decisions. Many electronic health records service providers provide a set of rules that govern their programs. Expert systems cannot work on their own since they need knowledge from medical experts.

But this AI technology is being phased out because expert system programs can break down if the rules are excessive, usually more than several thousand. Also, if the rules begin to contradict each other, the system will malfunction or break down altogether. If the system’s knowledge domain changes, you’ll need to change the rules, which is difficult and time-consuming. For these reasons, rule-based expert systems are slowly being replaced by data-based and machine learning algorithms.

Natural language processing

One of the main objectives of artificial intelligence researchers is to analyze human languages and break them down to rules that can be used in algorithms. Natural language processing, NLP, involves speech recognition, language translation, and text to speech analyses.  It can be approached from two points of view; statistical or semantic NLP.

Statistical NLP is based on deep learning neural networks and has heavily contributed to the recent accuracy in translation and speech recognition software. It needs a large body of language to learn from. In the medical field, NLP is used to analyze written and spoken medical notes, prepare clinical notes, transcribe patient interactions, and give conversational AI.

AI Machine Learning

Machine learning is a statistics-based AI technique that trains and fits artificial intelligence models with data. It is the fastest-growing form of AI and in 2018, 63% of companies surveyed were using machine learning algorithms in their businesses. Machine learning is the core of AI and there are many branches associated with it.

Here are some branches of machine learning in ascending order of complexity:

  • Supervised learning: This is machine learning that requires a training dataset with a familiar outcome variable. For example, a medical company can run tests on a program using a set of data to predict the onset of a disease.
  • Neural network: This machine learning technique is similar to a human’s neural processes. It analyzes problems as inputs and outputs, relates variables that affect the problems, and provides outputs based on these analyses.
  • Deep learning: The most complex form of machine learning. It basically involves many neural network models with tons of variables to predict multiple outcomes. Such models may have thousands of hidden features that are uncovered by today’s processing units and cloud-based storage systems.

What is Artificial intelligence in healthcare, and how does it apply to the medical industry?


Artificial Intelligence in healthcare is responsible for the growth of IoMT (Internet of Medical Things) already in use in healthcare facilities. This technological advance drives the shift from reactive to proactive patient care because the AI systems can provide better, faster, and more accurate feedback from patients.

IoMT also makes it possible for caregivers to develop therapies to meet the individual needs of each patient. This can be achieved through technologies such as blockchain, Big Data analytics, and sensors powered by Wi-Fi and Bluetooth. Such devices include Wi-Fi gateways and Portal Beams, to enable cloud computing for quick data processing and storage.

AI has the potential to revolutionize the medical industry, and enhance operations in service delivery.

IoMT will eliminate unnecessary information in the healthcare system, allowing doctors to focus on making the right diagnosis and treatment. But developers will need to provide standardized testing protocols to ensure the safety and efficiency of the AI systems.

The cost implications of AI in healthcare

Since the application of medical AI in healthcare is still growing, its cost implications have not yet been definitively proven. It still needs lots of extensive infrastructure and resources to establish machine learning and other AI-based technology in the medical world. Still, one of the aims of healthcare AI is to offset some costs incurred by the medical industry. This is done by automating most medical processes, reducing human errors and medical malpractice, AI-powered delivery of medical supplies, drug testing, and many other applications.

Diagnosis and treatment applications

Expert rule-based systems were able to make near-accurate diagnoses and treatment plans. They, however, were not any more precise than human diagnoses and integration with healthcare records and medical workflows was difficult.

Deep learning is currently being used in radiology and radionomy to identify cancerous lesions and detect details that are imperceptible by the human eye. This feature offers a more accurate oncology diagnosis.

AI is constantly being used by healthcare professionals to understand their patients’ day-to-day patterns and offer better feedback, guidance, and tips to staying healthy.

So far, there are various machine learning-based healthcare systems that apply cognitive reasoning to provide extensive medical knowledge and medical diagnostic tools. Some AI-powered systems work in tandem with medics and medical researchers to solve real-life medical emergencies.

Artificial intelligence in healthcare is a rapidly growing and dynamic field. It is an area with great potential for growth and with large benefits to patient care all around the globe. But it’s still important for the stakeholders in the healthcare industry to understand what artificial intelligence is in healthcare to have a firm grasp of its full potential in healthcare.


We at can help you harness the power of Artificial Intelligence in your healthcare facility to push your medical service delivery to the next level. We specialize in RTLS & IoT healthcare solutions and the integration of IoMT in your hospital to enhance operations and improve clinical outcomes.