Artificial intelligence (AI) and machine learning(ML) have helped optimize processes and workflows in many industries. In the healthcare sector, AI is increasingly helping in solving tough problems and uncovering hidden insights in the data generated by hospitals most of which is unstructured.

A steep increase in the amount of ailments and patients seeking medical care has led to a lot of medical data being generated in the form of images (X Rays, CT scans, PET Scans).

This medical imaging data, is a rich source of information that has paved the way for running successful ML algorithms. If this data was limited, it might not be feasible to generate meaningful insights from them.

Given the rate at which AI technologies are evolving, in the next few years we can expect huge problems in radiology and healthcare being solved very effectively.

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What is radiology?

The last time you visited a hospital for an X Ray, you might remember your doctor advising you to take an X Ray and making a diagnosis . The use of such imaging techniques to diagnose and treat diseases is called radiology and the people trained in such techniques are known as radiologists.

The most commonly used imaging techniques in radiology

X Rays Ultrasound CT scans (Computed Tomography) MRIs (Magnetic Resonance Imaging)

Machine learning in radiology, a few examples

a. Better Pneumonia detection

Also, machine learning can assist radiologists in detecting certain ailments quicker an more accurately that could be missed by the human eye.

For example, a deep learning algorithm built by Standford researchers could predict pneumonia from chest Xrays with a better accuracy than radiologists.

A radiologist discussing pneumonia predictions from chest X-rays with graduate students at Standford. Image source - https://stan.md/3b7ZhGs

Here is a live demo built on the Skyl.ai platform which you can use to upload a chest X-Ray and detect whether or not a patient has pneumonia. Skyl uses computer vision technologies and their image classification single class template to identify and scan for pneumonia.

Pneumonia detection using Skyl.ai

b. Better cancer detection

Google's AI based lymph node assistant Lynda was able to correctly able to detect cancer with a 99% accuracy. It was also able to accurately pin point the location of the cancer which could be too small to be detected in some cases by radiologists.

Google's AI based Lymph node assistant identifies the tumor region (right hand side image). Image source - https://bit.ly/3b2ojGV

Benefits of AI and machine learning in radiology

Radiologists usually have hectic schedules interacting with patients and other doctors. AI can help in reducing their day to day work load in the following ways by taking off certain routine tasks.

a. Better diagnosis

As seen in the examples before, AI can help in detecting ailments that are hard to detect using the naked eye. AI can lend a helping hand that can save precious lives.

b. Reduce routine tasks

Certain routine tasks such as classifying X Rays as normal and abnormal can be done with AI thus reducing workload of radiologists. Radiologists can spend time only in analyzing the abnormal X Rays.

c. Eliminate mistakes

Although well trained radiologists hardly make mistakes in diagnosing, AI algorithms can provide a second opinion to doctors. This can be really helpful in training newly recruited doctors.

d. Affordable healthcare

With AI, you can extend the benefits of modern medicine to remote areas where radiologists are hard to find. AI can function as a virtual radiologist in such places.

e. Increasing medical reach and reducing costs

Radiologists are well trained and accurate in reading and making predictions.Unlike the United states, skilled radiologists are difficult to find in certain countries.

In Nigeria, there are less than 60 radiologists for more than 190 million people which makes it 1 radiologist for 3 million people. In such places, AI can play a crucial role in helping increase healthcare reach at significantly lower costs.

Will AI replace radiologists?

Absolutely not. AI will always be an assistant automating routine tasks and helping them move on to focusing their time on tougher and more important tasks. The job role of radiologists in the future might change requiring them to have a basic know how of using AI and ML tools.