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Computer Vision in Radiology in 2024: Benefits & Challenges

Written by
Shehmir Javaid
Shehmir Javaid
Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple.

He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers.

He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations.

He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.
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The radiology industry faces various challenges, including revenue cuts, increasing workload, and a global radiologist shortage. Healthcare professionals in the radiology sector are opting for digital solutions to overcome these challenges.

The global digital radiology market is projected to reach $12 billion at the end of 2022 and grow to almost $15 billion by 2028.

AI is revolutionizing the radiology sector by making it more efficient and accurate, and one of the subfields of AI is computer vision (CV). This article explores the benefits of computer vision in the radiology sector and several use cases, and some challenges in implementing computer vision systems.

Benefits of computer vision in radiology

Radiology is all about testing or analyzing the human body through medical imaging. However, the human ability to analyze and process medical data is limited. For example, about 40% of breast cancers are missed by radiologists and clinicians.

Computer vision benefits radiology in the following ways:

Better diagnosis

Computer vision systems can analyze medical images such as CT scans, X-rays, MRIs, mammograms, etc., with greater accuracy and speed. This can improve, for example, cancer diagnosis by identifying cancers that are not visible to the human eye or retinopathy screening for diabetics to prevent blindness.

See how it works:

Reduced Workload

Computer vision systems based on sophisticated AI models are not yet mature enough to operate independently. Therefore, they should be developed to support radiologists rather than replace them.

Because a computer vision system can analyze medical images much faster, it can significantly reduce the workload of radiologists by allowing them to treat more patients. This can also reduce burnout rates for radiologists, which have been rising amidst the industry challenges. 

This can also be helpful in remote areas where the supply of radiologists is less; computer vision systems can assist existing radiologists in managing more patients.

Improve record-keeping

Radiologists usually have to manage a large number of medical documents; archiving and managing these documents can be time-consuming and inefficient. Computer vision systems enabled with document annotation can help overcome this issue by annotating documents for more efficient data processing and extraction.  

To learn about document annotation, check out this quick read.

See how computer vision and AI can assist radiologists and clinicians.

All these benefits can ultimately improve working conditions for radiologists, attracting more people to work in the sector and choosing it as a career path.

Challenges in implementing CV in radiology

Implementing computer vision systems can have various challenges. Some of those challenges are:

False-positive and negatives

As previously mentioned in the article, even though the accuracy of medical vision systems is quite impressive, it is not perfect. Computer vision systems in radiology still give false negatives and false positives. While analyzing mammograms or other medical images of life-threatening diseases, such inaccuracies can have dire consequences.

One of the key reasons for this weakness in current computer vision systems is the difficulty in collecting medical data. Due to its highly sensitive nature, medical data is difficult to gather for model training purposes.

Lack of generalization in medical images

Another major challenge is the nature of the medical images that are annotated for computer vision systems. Every human being has different shapes and sizes of organs; therefore, it is very difficult or almost impossible to create a training model which can cover all these variations.

For example, vehicle annotation is relatively easier to automate since almost all cars have edges and these edges are easier to generalize. This can be difficult in medical images since human organs’ size, shape, and color may vary from person to person. For example, people who exercise or excessively use substances and alcohol can have a larger or more swollen heart than others.

Difficult FDA approvals

While creating medical vision systems for radiology or other healthcare applications, the approvals of government regulatory institutions such as the food and drug administration (FDA) are difficult. The parameters on which these institutes test these systems and the set standards are difficult to meet considering the current limitations of the technology.

Expensive to train and implement

Another challenge in implementing computer vision in the radiology sector is related to its costs. Training one model can require thousands of high-definition images. These images are expensive to gather and annotate, making the overall development and implementation process costly.

As technology improves, developers are coming up with cheaper solutions to develop the medical image annotation processes; however, few of these solutions exist.

Watch this video to see one of the efforts of making the development process of a computer vision system in radiology cost-effective

To learn about the challenges of AI in the healthcare sector, check out our comprehensive article.

To achieve high-quality data annotation for your computer vision project, check out our data-driven lists of:

Further reading

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Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple. He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers. He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations. He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.

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2 Comments
Steve Smith
Jan 13, 2023 at 19:11

I’m glad you talked about how medical images could be identified properly using a vision system. The other day, my cousin told me he was looking for a digital linear accelerator that could provide cost-effective deinstallations for efficient servicing in their imaging center. He asked if I had opinions on the best option to find one. I’m glad about this enlightening article, I’ll tell him he can consult a well-known medical imaging system service for more details about the process.

Bardia Eshghi
Jan 20, 2023 at 05:06

We’re glad you enjoyed the article, Steve!

Mark Hamlin
Dec 22, 2022 at 22:09

Great read!! Thanks for sharing such a great blog. Blogs like these are really helpful.

Bardia Eshghi
Jan 20, 2023 at 05:15

Hi, Mark. We’re glad you enjoyed the article.

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