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Fascinating World of AI in Radiology: Its Use Cases and How to Approach AI-enabled Radiology Software Development

A radiologist holding your x-ray print in hand and examining it with narrowed eyes while you keep biting your nails and wondering if will he be 100% sure of what he would find in your x-ray.

Have you ever experienced such intense silence but wild thoughts while under the same roof as a radiologist?

If yes, let us tell you, you will not experience a similar thing again.

Now AI is reinventing the complete radiology practice by assisting radiologists in analyzing radiology images and diagnoses.

For both tech enthusiasts and radiologists, this phenomenon of computer programs spotting serious conditions; strokes, lung clots, brain bleeds, and others, is fascinating.

An Israel-based healthcare company named Aidoc has already proved that it can be achieved with a model – aggressively trained on past data of radiology images.

So, in this blog, let’s talk about AI in radiology, its use cases, and how Aidoc is making it possible.

This guide will help you a lot if you are planning to go for radiology software development.

First Thing First, Does Radiology Really Require AI?

Radiology is at the center of our healthcare ecosystem.

For any diagnosis, providers rely heavily on radiologists.

Over time, radiology workflows have become digital and more centralized.

However, it miserably failed to match up with the speed of primary and secondary care adopting the technologies.

And thus, the radiology industry is now at a crucial point.

If it does not now adopt modern-age technologies such as AI, machine learning, RPA, and computer vision, it will sink together with other healthcare specialties because everything is interlinked in healthcare.

So the answer to the question is, yes.

Radiology desperately requires AI technologies.


Well, there are multiple reasons why AI in radiology is inevitable.

But we are here sharing a few of them.

Reason 1: Image Volume Overload

Doctors have later realized that proper diagnosis is crucial for enhanced patient outcomes and quicker recovery.

Thus, they refer the majority of patients to medical imaging such as MRI, X-ray, CT Scan, etc.

Because of that, radiology departments of any healthcare setting submerge under the piles of image volume.

To cope with this, radiologists need to analyze and process hundreds of medical images every day which very negatively affects their wellness and work-life balance.

Let us share a number for your context.

Mayo Clinic alone analyzed over 9 million medical images in 1999 which reached 94 million in 2010.

And this number has been ever-increasing.

Radiology demand

Reason 2: Lack of Standardization and High Operating Cost

Healthcare entities are scaling up their operations.

The majority of hospitals are now becoming part of the healthcare system.

The average number of physicians they have is 76.

However, the speed of technology adoption is much slower than the speed at which they are scaling up their operations.

Such a disparity results in unstandardized and clogged-up processes which ultimately leads to high operating costs.

To overcome this challenge, the only workable solution they have is AI as it can put them ahead of all their challenges in a single shot.

In case you don’t know, one of the use cases of AI in radiology is standardization.

Reason 3: Poor Quality of Care

As per the research, one in every 5 patients feels that the quality of care is less than good.

A few major sources of dissatisfaction in patients are a very long patient care journey, inefficient diagnosis, frequent changes in care plans, and long delays.

With AI in radiology, we can eliminate all of these sources of patient dissatisfaction.

Because, AI makes diagnosis more accurate, frees up the radiologists, and assures quicker patient recovery.

Use Cases of AI in Radiology: Where AI is Used in Radiology and Radiology Software Development?

The best thing about AI is that it is limitless.

We can utilize it for any purpose we have even in radiology software development.

However, the major limitations are compliance, data, and limited human intelligence.

Yes, we have so far successfully discovered only a fraction of AI use cases in radiology.

Use case 1: Lesion Detection

A radiologist can leverage medical AI to detect lesions in uploaded medical images.

It happens in several different ways.

The first way is, a radiologist uploads a medical image and the AI system gives him back the results in the form of a list of suspicious areas in the image.

And the second way is, that a radiologist can select a specific area to hint to an AI program that it needs to analyze only that part of the image and detect lesions.

Radiology detection

Use case 2: Automatic Progress Tracking

The AI program can detect the change over time and present a detailed report of how with time, a failed organ or broken bone has either improved or worsened.

This enables radiologists and physicians to assess whether their care plan is working.

Use case 3: Diagnosis

This is perhaps the most important use case of AI in radiology.

If the AI program has access to the comprehensive information of the patient, it can provide diagnostic support in the form of a differential diagnosis.

However, it is still called adventurous to fully rely on AI diagnosis.

Because, the role of AI here is to aid physicians and radiologists, not replace them.

The way AI makes diagnosis easy is by comparing the uploaded medical image with the large data of historic medical imaging datasets and finding parities.

AI diagnosis

Use case 4: Risk Assessment

Assessing the clinical risk manually is limited to what that particular radiologist and physician have experienced recently in similar cases and what they have read and learned.

But when AI is assessing the risk, it takes the past decades of data into account and analyzes what could go wrong based on what it can observe from that historic data.

And as a result, some very rare but life-threatening clinical risks can pop up for each specific patient.

Since the risk is known to clinicians in a very early stage of the patient journey, it can easily be mitigated.

These game-changing use cases of AI in radiology can only be achieved through robust radiology software development.

So, now let’s talk about it in detail.

Radiology Software Development: How to Make it Work on AI?

One thing is very sure, if you want to make radiology software future-proof, it must work on AI technology.

Implementing AI has always been overwhelming, especially in the healthcare industry.

So, here we are sharing a few basics of AI implementation which you must know if you are planning to go for radiology software development.

A successful AI implementation can be achieved via an AI algorithm, dataset, and software or product.

1: AI Algorithm Layer

The essence of AI is to find meaningful patterns from historical datasets and compare the outcome with real-time data to predict the possibility.

When you train an AI model on a bigger database, it becomes more accurate and efficient in prediction.

However, you can never be able to analyze a big dataset which is needed to make an AI program more accurate.

Thus, AI algorithms are used.

Multiple AI algorithms cater to different use cases.

Developers select the most appropriate algorithm and run it through a big dataset to find patterns.

2: The Dataset Layer

One of the major limitations of AI in radiology is that it always requires data.

And even if we have data, we need to classify it properly to make sure that we are training the AI program on the right data.

This dataset exists in text and even images.

Datasets having data in text formats can be utilized for model training using any AI and machine learning algorithms.

But when there is data in the form of images, we have to utilize deep learning and computer vision technologies.

3: The software layer

This is where your radiology software lies along with the rules the AI model makes.

These rules are generally in the form of if…then.

The software program compares real-time data uploaded by the radiologist or physicians with the rules the AI model makes based on historical data.

Here, it is worth noting that the AI model keeps generating the rules because it keeps getting data.

It is an endless process.

Get Inspired by AIdoc which Takes AI in Radiology to the Next Level

Elad Walach (CEO), Michael Braginsky (CTO), and Guy Reiner (VP R&D) founded AIdoc in 2016 with a vision to leverage AI in clinical decision-making and make it so accessible that every clinician can utilize it.

So far, from a total of 9 funding rounds, AIdoc has raised a whopping $237.5 million. On June 16, 2022, the company bagged $110 million in its series D round from top investors Alpha Intelligence Capital, TCV.

The company offers 3 major AI solutions.

  • Radiology AI
  • Neuroscience AI
  • Cardiovascular AI

The best thing about AIdoc is that they have developed in-house AI algorithms for stroke, pulmonary embolism, cervical fracture, intracranial hemorrhage, intra-abdominal free gas, and incidental pulmonary embolism.

Using their solutions, radiologists and physicians can have additional data from medical images that are capable of drawing a line between time-consuming inefficient diagnosis and rapid accurate diagnosis.

The solutions are integrated with a prioritization tool to help clinicians detect urgent cases and quickly work on them with streamlined workflows for higher patient outcomes.

The company has so far helped the healthcare ecosystem outstandingly by taking both AI in radiology and radiology software development to the next level.


Let’s Get It Done. Building Your Next-Gen Radiology Software with a Healthcare-Specific IT Team

We’re an Ontario-based IT company.

And the one thing we and our clients are most proud of is that we only entertain healthcare IT projects.


Well, we understand healthcare and healthcare is something that makes our 50+ healthcare IT professionals happy.

More frankly, being a healthcare-specific IT company is what we choose to reinvent healthcare rather than feeling satisfied with one or two successful healthcare projects.

Our team of 50+ healthcare IT professionals includes UI/UX designers, developers, business analysts, compliance specialists, DevOps engineers, cloud specialists, and QA engineers.

Be it senior care, mental health, primary care, or even women’s health, we have delivered several mobile apps, web apps, and software to startups, hospitals, LTC homes, and enterprises.

Our healthcare solutions are always equipped with the fusion of two rare things – clinical value and peace of mind.

We’re known for our intense level of healthcare passion and our madness over healthcare.

So, if you are looking for a tech partner who takes healthcare very seriously, let’s talk.