How AI Can Be Used in Healthcare

While the concept of artificial intelligence (AI) may seem intimidating, if not dangerous, to some, there’s no denying that AI assists in every area of our lives, making things so much easier and more convenient. From self-driving cars and SIRI to mobile banking and fraud prevention, AI has been in use for quite a while now. Its applications are indeed far more common than one might imagine.

It’s not just tech companies that are pioneering AI solutions. Today, governments and healthcare providers continuously test and invest in AI practices. In the medical industry, AI is revolutionising health treatments and care services.

Continue reading to learn more about AI and its applications in the medical industry.

3 Ways Artificial Intelligence (AI) Becomes a Transformational Force in the Healthcare Industry

Machines make human muscles a thousand times stronger, and they make the human brain a thousand times more powerful. It merits repeating that artificial intelligence is becoming a force to be reckoned with, especially in the medical world. Providers and patients alike benefit from the impact of AI-driven tools in various ways. Let’s explore some of them in this section.

It Unifies Mind and Machine

Communicating with the use of computers and smartphones is by no means a novel idea. Unifying mind and machine is. With AI, it becomes possible to create direct interfaces between technology and the human mind without the need for keyboards and monitors. This is a cutting-edge area of research that has major applications for some patients.

Consider neurological diseases and trauma to the nervous system. Patients diagnosed with such diseases may lose their ability to speak, move, and interact meaningfully with people and the environment. Those who feared their fundamental experiences lost forever could still restore them with brain-computer interfaces (BCIs).

This AI solution allows experts to decode the neural activities associated with the intended movement of one’s hand. Using ubiquitous communication technologies (like a phone or tablet computer), a person can communicate the same way as many people in the room have communicated at least five times throughout the morning.

It Develops the Next Generation of Radiology Tools

MRI machines, CT scanners, and x-rays can obtain radiological images. They offer non-invasive visibility into the inner workings of a patient’s body, allowing a clinician to give a more accurate diagnosis. However, some diagnostic processes still rely on physical tissue samples obtained through biopsies. While these processes are generally safe, they carry risks like the potential for infection.

AI enables the development of radiology tools with cutting-edge technology. Some experts predict that these tools are accurate and detailed enough to replace the need for tissue samples in some cases. To achieve very close registration for any given pixel, experts are continuously developing imaging technologies that give the same information to those obtained from tissue samples.

It Reduces the Burden of Electronic Health Record (EHR) Use

Electronic health records are an important part of the healthcare industry’s journey towards digitalisation. However, the switch from manual to electronic records has brought myriad problems associated with endless documentation, cognitive overload, and user burnout. Today, developers are using AI to create more intuitive interfaces. As some of the routine processes get automated, users can save much of their time.

From clinical documentation, order entry, and sorting through the in-basket, EHR users spend the majority of their time on these three tasks. Dictation and voice recognition improve clinical documentation. Natural language processing (NLP) also take things up a notch.

As of writing, experts are still working on bolder innovations like video recording a clinical encounter. AI and machine learning can index these videos for information retrieval in the future.

Machine Learning: Improving Patients’ Care Plans

Machine learning and other forms of artificial intelligence are providing clinicians with the best evidence-based care pathways and enhancing patients’ care plans. To offer clinically meaningful services, medical professionals and institutions use social determinants of health combined with machine learning. By merging evidence-based care paths with historical utilisation and outcomes, optimal patient care is achieved.

Does It Work?

To anyone unfamiliar, the concept of machine learning, which is used to enhance patients’ care plans based on personalised analytics, may seem complicated. In the first place, first-rate patient care is quite difficult to achieve. How can the general population trust machine learning to combine real-world historical utilisation, outcomes, and the latest literature with evidence-based care paths? If anything, the concept of an AI-dependent health care system only brews storms of data privacy concerns and fears of mismanaged care due to machine error.

This is where evidence-based guidelines come into the picture. Such clinical guidelines are an integral part of an intelligent care path solution. They are the starting point for care plan models and are written for the average patient. As these guidelines can’t accommodate all the comorbidity permutations that exist for patients of high acuity, machine learning is used to help.

Some patients simply don’t fit existing evidence-based care paths. With artificial intelligence, clinicians can use machine learning models to infer what has been the most efficacious path for diagnostically identical patients from historical data.

How Machine Learning Came to Be

In the past, clinical data analysis was a drawn-out process. It wasn’t only a big data problem but a messy one, too. That’s mostly because much of the valuable information wasn’t readily available in a structured form.

There’s also the thing with volume. It’s among the top reasons why we use machine learning. When clinicians and healthcare organisations end up with thousands of attributes, they need machine learning to identify the variables that are driving their care model and their patients’ social determinants of health (SDOH) data.

Machine learning becomes crucial for variable selection. It also plays a big part in refining some signals from the noise of operational clinical data.

How Is It Used?

Typically, data on patient lifestyle and their SDOH – social determinants of health – are not captured in standard electronic health records. However, most diligent clinicians refer to this type of data in their clinical notes.

Machine learning seeks to supplement existing care models with SDOH data. Whether on lifestyle or health state, it incorporates patient-reported data and points access to other patient challenges. Such type of artificial intelligence technology ultimately improves doctor and patient experiences.

Examples of Machine Learning in Medicine Across the World

The healthcare sector has benefited from technological advances like machine learning, a subset of artificial intelligence. From the development of new medical procedures and the handling of patient data and records to the treatment of chronic diseases, machine learning plays a huge role in many health-related realms. By generating precise medical solutions customised to individual characteristics, machine learning can help uncover the best possible treatment plans.

Indeed, this technology is the future of healthcare. Here, we’ll go over the applications of machine learning in the healthcare sector and also the many companies from around the world that harness its power.

Smart Records

Quotient Health

Located in Denver, Colorado, the team over at Quotient Health developed software that aims to reduce the cost of supporting electronic medical records systems. Optimising and standardising the way those systems are designed, the one true goal of this software is improved care at a lower cost.

Ciox Health

This company in Alpharetta, Georgia uses machine learning not only to enhance health information management but also the exchange of health information. The goal is to modernise workflows, facilitate access to clinical data, and improve the accuracy and flow of health information.


Kensci is a company based in Seattle, Washington. It uses machine learning to predict illness and treatment, helping physicians and payers intervene earlier. It can also identify patterns and surfacing high risk markers, model disease progression, and more. With Kensci’s platform, clinicians and researchers can predict population health risk from relevant data.


A smart innovation from Shenzhen, China, ICarbonX offers a platform that uses AI and big data to look more closely at human life characteristics. This is done in a way the team describes as digital life. ICarbonX hopes its big data will become powerful that it can manage all aspects of health by analysing the health and actions of human beings in a carbon cloud.

The company believes its technology can gather enough data to help clinicians and researchers better classify symptoms, develop treatment options, and ultimately get patients healthier.

Medical Data

Concerto Health AI

Located in New York, machine learning courtesy of Concerto Health AI analyses oncology data that provides insights to oncologists, pharmaceutical companies, payers, and providers. This allows them to practice precision medicine and health. With its recently launched platform called Eureka Health Oncology, Concerto Health AI uses deep data from electronic medical records (EMR). This platform offers AI solutions for the efficient management, delivery, and use of clinical data.

MD Insider

Launched in Santa Monica, California, the MD Insider platform harnesses machine learning to better match patients with doctors and specialists. This is a helpful platform to enhance physicians’ referral capabilities. With a platform like this, patients can easily find the right specialist or practitioner that can address their medical concerns.

Orderly Health

Developed in Denver, Colorado, Orderly Health’s platform is an automated 24/7 concierge for healthcare. The platform allows people to understand their healthcare benefits and locate the least expensive providers, helping employers save money.

Medical Imaging, Diagnostics, Drug Development, and Treatment

Deep Genomics

With its official launching in Toronto, Canada, Deep Genomics offers this AI platform that helps researchers find candidates for developmental drugs for neurodegenerative (e.g., Alzheimer’s disease and Parkinson’s disease) and neuromuscular (e.g., amyotrophic lateral sclerosis, multiple sclerosis, myasthenia gravis) disorders. By finding the right candidates during a drug’s development, pharmaceutical companies and researchers can raise the chances of successfully passing clinical trials and at the same time decrease time and cost to market.

This platform by Deep Genomics also analyses over 69 billion different cell compounds. Researchers can receive feedback in real-time, making the drug development process quicker.

Benevolent AI

Benevolent AI was first launched in London, England. Its primary goal is to get the right treatment to the right patients at the right time. Taking advantage of artificial intelligence, it produces a better target selection. The platform also provides previously undiscovered insights through deep learning.

Working with major pharmaceutical groups to license drugs, the team over at Benevolent AI is also partnering with charities for the development of easily transportable medicines for hard-to-treat and rare diseases in the areas of immunology, neurology, and oncology.

Zebra Medical Vision

This AI-powered radiology solution was developed in Shefayim, Israel. It provides radiologists with an AI-enabled assistant that receives imaging scans. Afterwards, it analyses them for various clinical findings it has studied. These findings are then passed onto radiologists. From there, they consider AI-enable assistant’s reports when making a diagnosis.

How AI Is Used in the UK’S National Health Service (NHS)

Health Secretary Matt Hancock mentioned sometime in November 2018 that AI will play a crucial role in the future of the NHS. Plans to transform the health service over the next ten years or so involve harnessing AI and its potential. How does an AI-enable NHS pan out? Find out how AI is used in the NHS in this section.

InnerEye System in Addenbrooke Hospital

This hospital in Cambridge is using Microsoft’s InnerEye system – a software solution that automatically processes scans for patients with prostate cancer. The scans are anonymised and patient data is kept private. The system works by taking a scan image, outlining the prostate on the image, marking up tumours, and reporting back. In other words, it speeds up prostate cancer treatment. Addenbrooke Hospital is looking to use the same technology for patients with brain tumour.

HeartFlow’s AI Technology

This system is also being used in the NHS. It analyses CT scans of patients who are suspected of having coronary heart disease. HeartFlow’s technology creates a personalised 3D model of the heart that shows how blood is flowing around it.

C the Signs App

In Merton and Wandsworth, GPs and practice nurses are pushing for the “C the Signs” app. The technology helps professionals check combinations of signs, symptoms, and risk factors during patient consultations. Medical professionals can identify patients at risk of cancer earlier.

Harness the Power of Technology with StratHealth Ltd

Here at StratHealth Ltd, we focus on harnessing the vast knowledge of our colleagues and partners to take advantage of new organisational models for healthcare delivery. We are here to help grow healthcare providers and organisations. Get in touch with us to discuss how we can help your company or organisation grow.


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