AI in radiology: top 10 use cases and best practices

AI in radiology: top 10 use cases and best practices

November 23, 2022

Within the past decade, rapid technological advancements have happened in both medical imaging and artificial intelligence, causing the two to converge. 

Radiology is a medical specialty that uses imaging technology to diagnose and treat illnesses. AI-based systems can help radiologists spot anomalies, detect tumors, and diagnose various diseases more accurately, which boosted their widespread adoption.

According to a recent study, there is a significant workforce shortage of radiologists: 1.9 radiologists per million people in low-income and 97.9 in high-income countries, respectively. UK researchers asked expert clinicians to classify more than 3,600 images of hip fractures. The study showed that clinicians recognized only 77.5% of images accurately, while the machine learning system achieved 92% accuracy. In a nutshell, the ever-growing demand for radiology coupled with increasingly more accurate AI-based radiology systems makes AI a saving grace for healthcare worldwide.

AI in radiology market statistics

of radiologists use AI in their clinical practices

American College of Radiology

plan to purchase AI tools in the near future

American College of Radiology

AI in the global radiology market CAGR from 2021 to 2030

Vision Research Reports

Chart title: Artificial intelligence in radiology: market size forecast, 2021-2030 Data source: Vision Research Reports

10 AI use cases in radiology

The number of use cases of artificial intelligence software in clinical data science and radiology practice is growing. Let’s discuss the top 10 applications of AI in radiology:

Enhancing cardiac imaging

AI has multiple applications when it comes to cardiac health. One such application is predicting which patients could develop irregular heartbeats by analyzing electrocardiograms. Another application of AI lies in cardiac imaging. AI can enhance visualization of the heart by coloring heart chambers on grayscale echocardiography images in real-time, which facilitates radiologists' work. Philips developed an AI-based system, HeartModel, that is able to produce a colored heart model and project its dynamic 3D representation with wall motions and LV and LA volume changes during the heart cycle.

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Examples of AI in radiology


Viz, a medical imaging company that is dedicated to optimizing emergency treatment using deep learning technology, developed Viz LVO, a clinically validated AI-based technology that can automatically detect and triage LVO strokes. In 2018, Viz LVO was approved by the FDA and consequentially created a new category of medical devices: computer-assisted triage. The device can automatically analyze computer tomography angiography (CTA) of the brain and identify LVO. A peer-reviewed study found that Viz LVO has a high sensitivity of 96.3% and specificity of 93.8%, making it a reliable companion for neuroradiologists.
Image title: The Viz LVO device offers automatic stroke detection and notification (left), coupled with a mobile DICOM viewer (right) Data source: — AI-powered stroke triage system performance in the wild, 2020
The Viz LVO device offers automatic stroke detection and notification (left), coupled with a mobile DICOM viewer (right)

10 benefits of AI in radiology

Early detection

Improved prioritization

Improved accuracy

Optimized radiology dosing

Reduced radiation exposure

Enhanced image quality

Improved satisfaction

Faster diagnosis

Improved access to care

Improved reporting

AI in radiology

AI can more reliably detect diseases at early stages, preventing complications and dramatically improving patient outcomes
AI-based radiology tools can automatically prioritize scans based on case severity, saving clinicians’ time and ensuring patients receive timely care
Most radiology AI tools can detect abnormalities more accurately than human radiologists, increasing patients’ chances for recovery
AI dose optimization systems can help reduce the radiation level patients are exposed to during a scan by minimizing the radiation dose
AI can help reduce the amount of radiation exposure by providing more accurate images with less need for repeated imaging
AI can help improve the image quality of medical scans, facilitating abnormalities detection and diagnostics
AI-powered radiology tools can help improve patient satisfaction by providing faster and more accurate diagnoses
AI can help speed up the diagnosis process, allowing patients to receive treatment more quickly
By improving patient throughput and making decisions without human involvement, AI can democratize access to radiology worldwide
Most AI-powered radiology tools automatically produce error-free and standardized reports, saving time and streamlining workflows
Early detection

AI can more reliably detect diseases at early stages, preventing complications and dramatically improving patient outcomes

4 challenges with AI in radiology and potential solutions

Aligning medical guidelines with AI outputs



Most current AI applications in radiology provide estimates of how likely a certain patient is to have complications based on radiological imaging. For example, an AI system concludes that a breast lesion of a certain patient has a 10% chance of being malignant. A radiologist could opt for a biopsy, but the AI system may not understand the severity of the problem and deem a 10% chance of cancer insignificant for conducting a biopsy.

It is critical for developers and medical professionals to work in close collaboration. Insights from medical professionals can improve the performance of AI-based solutions.

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AI methods in radiology

AI has become synonymous with machine learning (ML), which encompasses many techniques used to create predictive models, including deep learning models. Today, the most popular way of mimicking human decision-making is artificial neural networks (ANN), which is a certain type of very flexible deep learning model. 

ANN is inspired by the design of biological neural networks. In other words, this model tries to mimic the network of neurons that comprise the human brain so that it can solve problems similar to the way a human would. 

Currently, the convolutional neural network (CNN), which is a class of ANN, is the most popular model for analyzing visual data, making it the most important component of AI in radiology.

Input layer
Hidden layer 1
Hidden layer 2
Scheme title: CNN operation sample Data source:—Artificial intelligence in radiology: friend or foe? Where are we now and where are we heading?

Implementation tips for AI in radiology

Build a multidisciplinary team

While input from field experts is always critical to the development of AI solutions in virtually any industry, medical imaging calls for radiologists to have a much more decisive and significant role in the development of AI-based radiology solutions. Engineers and data scientists lack an understanding of essential radiological and anatomical concepts, which is crucial to creating AI systems applicable in radiology. On top of that, radiology is a very nuanced medical field, where therapeutic methods tend to change every few years. It’s highly advisable to involve radiologists in every phase of development.

Gain support from leadership

Healthcare providers should get key decision-makers and executive leadership on board with AI implementation. According to the latest report by Deloitte, healthcare providers who succeeded in AI implementation put leaders at the core of AI transformation. These leaders should clearly communicate with the radiology department on how new tools will impact their daily workflows.

Prioritize AI explainability

In a recent study, radiologists received chest X-rays and diagnostic advice from human experts and from AI systems. The radiologists rated AI advice as lower quality, which highlights the persistent 'AI black box' problem. In healthcare, where decisions can directly influence one's well-being, it is paramount for radiologists to understand the underlying decision-making of AI systems. Most AI radiology tools use deep learning models, and system interpretability and transparency become crucial. While deciphering deep learning models is a universal task across many AI applications, developers are responsible for minimizing bias and making models as explainable as possible. It comes down to continuous algorithm validation, dataset audit, evaluation of performance, standardized documentation, and routine bias assessment.

Add AI into your radiology practice

Add AI into your radiology practice

AI-based radiology tools are already used in clinical practice every day, and their role will increase moving forward. The validation of existing AI applications and development of new ones for solving common radiological problems requires effective collaboration between radiologists, data scientists, and engineers.

AI will make the next two decades transformational for healthcare and radiology in particular. Our professional AI consultants can help you implement AI-based technology into your radiology practice.

Add AI into your radiology practice


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