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How Artificial Intelligence (AI) is Transforming the Field for Radiologists

Artificial Intelligence is a branch of computer science that mimics the human brain.

With AI’s help, many tedious tasks are being completed on the spur of the moment.

AI is used in almost every field today.

From pattern analysis in finance to handwriting recognition in crime, from fingerprint and speech recognition in intelligence to building websites in information technology.

AI is everywhere.

Pharmacists are using AI-based robots to fill blister packs.

Can you use AI in radiology?

Radiology is a vital part of the healthcare industry.

It is the first step for a patient to find out if they are ill.

Can you trust AI in such delicate matters?

The answer is yes.

In this blog, we’ll discuss using artificial intelligence in radiology.

Along with that, we will also shed light on how it is transforming radiologists’ lives.

The Use of AI in the Diagnosis Process

Diagnosis is the initial part of any treatment.

If the problem is not identified, then how can there be a cure for it?

Machine technicians or radiologists usually carry out problem identification in the human body.

Radiologists see the scan in the digital system and then apply a set of filters to the scan before sending it out for printing on the X-ray film.

If the digital process and the analysis are done in the digital system by itself, then half the workload of the radiologists will be reduced.

If AI does all this work, it will be easy for radiologists to focus on more vital tasks they may have.

With the use of AI in radiology, the work becomes easy but also brings fear.

The fear of radiologists being replaced by AI technology.

We will discuss it as we go further into the article.


The Merits of Using AI in Radiology for the Diagnosis

Radiologists are by far the busiest healthcare professionals.

They need to be observant, smart, and quick in their analysis.

They possess a wide range of clients with whom they’ll have to interact daily.

These clients include hospitals, specialists, independent practitioners, cardiologists, urologists, orthopedics, and so on.

They need to be sharp, always, and they can’t make a mistake as their input is crucial to the patient’s life.

AI can help them maximize their time by eliminating some of their tasks. Some of the benefits of AI in radiology are:

1. Identify Repetitive Patterns

It becomes easier to find repetitive patterns with AI as it keeps the report of all the data given to it as input and learns based on that data.

AI helps to spot even a slight abnormality easily when it may escape the eyes of the radiologist.

As radiologists have multiple images to check, they may need to catch the repetitive pattern.

2. Provide a Second Opinion

By integrating AI into the digital system of diagnostic machines, you can get readings faster.

Along with it, you can even get a second opinion from a radiologist.

With this approach, you can get surety about the diagnosis that was made out of your report.

You won’t have to go anywhere else just to get a second opinion on the test results.

3. Offer a Differentiated Diagnosis

With the use of AI in diagnosis, the reports can add information to it.

The information is usually the average readings of a normal patient, but it’s difficult to identify the parameter whose reading is displayed on the reports.

4. Eliminate the Chance of Variability

A radiologist is bound to identify abnormalities in the reports.

Sometimes, there can be an error in intercepting the meaning of the report.

Radiologists know they must give their best, but sometimes something may happen during the day.

With AI, no such things can happen.

What are the Types of AI algorithms that can be used in Radiology?

AI can work based on multiple algorithms, but when things concern healthcare, it has got 2 main algorithms for identifying any abnormality in the images.

One of these two algorithms uses only an image as input, while the other one has other patients’ data added to it.

1. Use Only the Image as an Input

If only an image is given as an input to the algorithm, then it will learn and train the algorithm to decipher the image (data) based on the pixels or voxels present in the image.

It can either make an average of the neighboring pixels to get the desired pixel or identify the image with the help of the boundary pixels surrounding the abnormality portion of the image.

2. Add Other Data about the Patient along with Input

Suppose another set of patient data is given to the algorithm along with the input data.

In that case, the algorithm will compare the data and provide the analysis based on the other data of the patient.

If multiple data are fed to the algorithm, it will compare the input data with each data given to the system.

For example, if pathology lab reports are also given to the AI system, it will provide the analysis after considering every aspect, which becomes tedious for radiologists.

With the help of AI in radiology, this type of complex task can be completed by just giving the data to the system.

These methods make it easy for radiologists to identify patterns and analyze the images.

Though radiologists possess a wide scope, they fear AI may snatch their place from the industry.

What if they start using AI as their assistant and work together with it?

Let us discuss it in detail.

Will Radiologists Have to Switch Jobs Once AI is Introduced in the Diagnostic Field?

When any diagnostic equipment like an X-ray takes a picture of the injured part of the body, the picture is seen on an X-ray film after some time.

Radiologists then analyze this film.

Radiologists have to go through thousands of images in a day.

As image recognition is one of the subfields of AI, what if it can even analyze x-ray films?

What will the radiologists do?

Will they have to switch their jobs, or will they embrace AI with open arms?

Radiologists who use AI in their work will not have to worry about anything, but the ones who don’t use AI in diagnosis will have to think about upgrading themselves.
Let us first understand the role of radiologists once AI is all set in the diagnostic field.

If the data is fed into the deep learning algorithm, it will continue learning from the experiences.

The radiologists will supervise the data and can perform other tasks when AI checks the images.

The radiologists will have to just focus on the more prominent tasks.

If there are complex images, radiologists will analyze them.

Clinical Applications of AI in Radiology

AI is increasingly important in healthcare, including pattern recognition, drug discovery, risk management, remote patient monitoring, virtual assistants, wearables, DNA and RNA sequencing, etc.

The fields relying on imaging data have already started using this technology in their tasks.

Let us look at some of the clinical applications of AI in Radiology.

1. Thoracic Imaging

Lung cancer is one of the most common types of cancer.

Its screening can help identify the pulmonary nodes, which can help in the early detection of the disease.

It can be lifesaving for many patients. Artificial Intelligence (AI) can assist in identifying these nodes and categorizing them into benign or malignant.

2. Colonoscopy

If colonic polyps are not detected or misclassified, it can pose a potential risk of colorectal cancer.

Constant monitoring is vital in such conditions as these polyps are benign initially.

They become malignant with time.

AI can help in early detection with consistent and robust AI tools.

3. Abdominal and Pelvic Imaging

Computed tomography (CT), and magnetic resonance imaging (MRI) have become more accurate.

They can help identify liver lesions.

AI can be used to classify these lesions into benign or malignant categories.

4. Mammography

Screening of Mammography is challenging for experts.

AI can help identify and categorize the small depositions of calcium, often called microcalcification, in the breasts.

5. Radiation Oncology

Segmentation of tumors plays a huge role in determining the patient’s radiation dose and treatment plan.

For evaluating the success rate of the therapy, response assessment by monitoring the patient is essential.

AI can perform these assessments and improve the speed and accuracy of the treatment.

6. Brain Imaging

AI can be used for making diagnostic predictions for brain tumors.

The abnormal growth of the brain tissues generates brain tumors, which can be benign, primary, malignant, or metastatic.

As imaging data is collected during routine checkups, a large data set is available for scientific and medical discoveries.

Radiographic images and clinical data outputs have led to the rapid expansion of radiomics as a field of medical research.

Though AI benefits many aspects of the medical field, it still has a long way to go.

There are various challenges to overcome before AI is fully applied and widely adopted in the field of radiology.

Current Challenges for AI in Radiology

The sky is the limit when it comes to the use of AI in radiology.

It has simplified many complex tasks and is continuing to do so by trying to overcome the challenges mentioned below.

1. A Sufficient Amount of Quality Labeled Data

In the medical field, acquiring high-quality labeled datasets is quite complex.

When compared with other fields, the medical field has several limitations with the data that can be shared.

For example, if we compare a medical imaging dataset of 1000 images to a non-medical database which may contain 100 000 000 images.

The available volume for high-quality data is way too small.

By using augmentation, this problem can be tackled.

2. Dealing with 3D Reality

Successful deep-learning models are trained on 2D pictures, and medical images add an extra dimension to CT and MRI images.

Most of the current deep learning algorithms need to be adjusted to these images, and experience and expertise will be required to apply these algorithms in deep learning.

3. Image Acquisition (Non-standardized)

Non-standardized acquisitions of medical images are challenging for training the artificial intelligence algorithms in radiology.

Various data is required, and a large dataset helps the deep learning network build a robust algorithm.

Transfer Learning, a preprocessing technique that overcomes the acquisition and scanner specifics, can solve this problem.

4. Smooth User Experience

Radiologists often mention their dissatisfaction with the current software experience.

The reason is that the software could be more user-friendly.

It takes too much of their time, and they must keep the manual themselves once they have opened the program.

Building user-friendly software is necessary for companies and organizations that want their software used in the clinic.

But it is more complex than it sounds.

Many companies start to build an algorithm first and then turn it into a product.

Testing your product’s or software’s user-friendliness should be a vital part of the product development process from the initial stage.

It ensures that the radiologists will start to use it in the clinic and continue to do so.

We recommend building custom software that meets all the needs of the radiologists and the organization.

5. User’s Trust in AI Radiologists

The most significant challenge for AI Radiologists is the need for more trust in artificial intelligence.

AI is seen as a “black box” when it comes to answering questions related to the analysis of medical images.

With the help of scientific research and installing the software in the hospital, the user’s trust can be gained.

The other way to do this is to make the radiologists and the physicians aware of the datasets used for the output or analysis.

Don’t Let Data Breaches Hold You Back from Automating Your Radiology Processes. Let us Help You Build a Secure Solution for Your Organization.

Are you a radiologist looking to improve your daily tasks and offer better patient care?

AI techniques in radiology have shown promising outcomes, from rapid image processing to providing a second opinion.

AI can help identify tumors and microcalcification, recognize complex patterns, and categorize benign and malignant cells, tissues, and tumors.

It can even optimize the radiation dose given to the patient.

While data breach is a significant concern in automating medical processes, it doesn’t have to be.

By building custom software solutions for your organization’s needs, you can ensure the security of your data and avoid potential HIPAA breaches.

If you’re ready to take advantage of the benefits of AI in radiology and improve patient care, let’s connect today to discuss how we can help you build a secure and effective solution for your practice.