5 Examples of AI in Healthcare [& Why You Need It]

5 Examples of AI in Healthcare [& Why You Need It]

Artificial Intelligence is rapidly becoming an important part of healthcare. The size of the Health AI market which was $600M in 2014 is projected to rise to $6.6B by 2021 according to research by Accenture. That is a growth of nearly 1000%!

AI based technology is helping healthcare in activities right from consultation to recovery. Patient health monitoring via wearables like a smartwatch, e-consultation, online appointments, patient flow tracking within hospital premises, robot-assisted surgeries, identifying patient characteristics for clinical trials, and genome decoding, are just some of many uses of AI in healthcare.

In this article, we give you 5 examples to indicate just how powerful and useful AI really is for healthcare.

P.S. – If you’re looking to get a healthcare application developed, drop us a message for a free consult (pun intended).



PathAI is developing machine learning technology to assist pathologists in making faster and accurate cancer diagnosis for patients. The tech is also going to help identify patients that benefit from novel therapies, to make scalable personalized medicine a reality.

A pathologists job in cancer diagnosis is to identify cancerous cells, which can be as hard as finding a needle in a haystack. Doing this for multiple patients in a day adds to the difficulty.

PathAI aims to use AI to improve this identification process, resulting in faster diagnosis, and more importantly in accurate diagnosis.



Buoy Health is an AI-based chatbot that works as a symptom checker. You start by typing in what you’re experiencing. Buoy guides you through a series of questions based on your responses and then helps with causes to severity and clinical insight into what’s going on. You can also connect with patients in the community with similar symptoms.

A healthcare facility can use such a first line of diagnosis to streamline patient care, and also to collect relevant and useful patient information.



Enlitic is a San Francisco-based company that uses data to advance medical diagnostics. The deep learning platform analyzes unstructured medical data (radiology images, blood tests, EKGs, genomics, patient medical history) to give doctors better insight into a patient’s real-time needs. They achieve this by pairing radiologists with data scientists and engineers to collect and analyze comprehensive clinical data that enhances the accuracy of the platform.

Enlistic works across different areas of diagnosis. For example, it helps radiologists triage scan by analyzing and interpreting them for urgency. It also helps with post-read analysis, checking a radiology report against the corresponding images to help prevent over or under-called findings.



One of the most common reasons for death among cancer patients is catching the disease too late. Freenome is a platform that performs tests on samples to detect cancer cells early.

According to Freenome – By combining deep expertise in molecular biology and advanced computational techniques to recognize disease-associated patterns among billions of circulating, cell-free biomarkers, we are developing simple and accurate blood tests for early cancer detection and integrating the actionable insights into health systems to operationalize a feedback loop between care and science.

Freenome could save millions of lives by helping the medical community diagnose potential cancer threats early enough for successful treatment.


Beth Israel Deaconess Medical Center (BIDMC)

BIDMC released a research paper stating an AI-enhanced microscope system is “highly adept” at identifying images of bacteria quickly and accurately. This could help clinical microbiologists diagnose potentially deadly blood infections faster and improve patients’ odds of survival.

The neural network of the AI enhanced microscope was fed more than 25,000 images from blood samples prepared during routine clinical workups for training. By cropping these images (in which the bacteria had already been identified by human clinical microbiologists) the researchers generated more than 100,000 training images. The machine intelligence learned how to sort the images into the three categories of bacteria (rod-shaped, round clusters, and round chains or pairs), ultimately achieving nearly 95 percent accuracy.

Looking to Digitize Your Healthcare Service?

AI in healthcare is the next generation of diagnosis which is assisting doctors, radiologists, pathologist and practitioners achieve results faster and more accurately.

If you are looking to build a digital product or incorporate an existing one into your healthcare facility, Getafix can help. Drop us a message and let’s get the conversation started.

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