AI in X-ray Analysis: Benefits & Challenges in 2024

X-ray is the most common form of medical imaging: it is estimated that 3.6 Billion X-ray images are taken each year. 45% of radiologists report burnout due to reasons such as time pressure and the rising volume of scans. AI in analyzing and reporting X-ray results can have an impactful effect on radiology. In this article, we’ll go over the benefits of leveraging AI in X-ray analysis and provide recommendations for several challenges in implementation.

Benefits

Decreases the workload 

Artificial intelligence increases the speed of anomaly detection significantly as it can analyze images much faster than a human. Manually analyzing X-ray images is a labor intensive process and might lead to decision fatigue and incorrect diagnosis. AI can help decrease the workload of radiologists, lower burnout rates and allow radiologists to focus on patients that need more attention. 

The first autonomous X-ray AI that is approved by the EU for medical use can automate up to 40% of reporting workflow. 

Increases access in developing countries 

The shortage of radiologists in remote locations and developing countries can be addressed by using AI in X-ray analysis. For example, tuberculosis is a major issue in developing countries. Given the resource constraints in those countries, AI models that detect tuberculosis can add significant value in terms of cost and life saving. 

You can watch this video to understand how AI is used in X-ray analysis to speed up the diagnosis process:

Better diagnosis 

X-ray AI models have been found to be more effective in detecting certain diseases compared to doctors. It has been found that lung cancer detection can be improved by using X-ray AI. AI also has outperformed radiologists in detecting tuberculosis.

You can check some of the real-life use cases of AI in X-ray in the following video:

Challenges 

Development problems 

Data gathering

ML models require a substantial amount of data in order to learn and function properly but finding medical data can be challenging due to patient data privacy laws.

Recommendations: Leverage data augmentation and synthetic data generation techniques to expand datasets without collecting new data points. 

No standardized image acquisition

Because of different scan machines, lenses, and acquisition settings, there is no standardized image acquisition process when it comes to medical images. This could limit the use case of many algorithms as they rely only on one source of data which makes them less effective and not usable in a clinical environment. Research has found that almost all AI algorithms for medical image analysis lack the robustness recommended for clinical use.

Recommendations: Diversify the data sets. Using data sets from different sources increases the robustness of the algorithm but it comes at the cost of larger data collection requirements. 

Data labeling 

Most AI models require labeled training data that describes what is in the image. Labeling radiological images can be a time-consuming task if done manually and require experts and specialists. 

Recommendation: Use data labeling tools with a focus on medical data annotation. 

You can check our data-driven list of medical image annotation tools here.

Transition

AI has been used mostly in research but in order to utilize its full potential, it needs to transition to clinical workflow. Integrating AI with the digital and informational environment that radiologists use remains a challenge. 

Trust

The majority of radiologists were happy with their overall experience and thought AI was beneficial to them and to their patients but 95% of them would not trust AI to run autonomously. One of the main reasons for the lack of trust in AI is the black box problem, developers should consider the fact that radiologists need to understand the decision-making process of the AI and not just the end result. 

Recommendation: Using explainable AI models with clear user interface design that shows the decision-making process of the AI can improve trust in AI systems.

Regulatory barriers

Regulatory clearance is required for medical devices to be deployed and used. CE approval is required In Europe and FDA approval is required in the US. Getting this approval requires satisfying certain standards which can be hard to meet.

However, recent years have been showing a promising trend in the approval of AI/ML both in the EU & USA. The number of AI/ML medical devices that are approved each year is increasing and in 2021 the biggest portion of it is coming from radiology devices. 

Source: Researchgate 

Recommendation: Follow the regulatory framework for developing AI/ML for medical devices from the start. FDA and EU have published their framework and guideline on AI/ML medical devices. 

Cost

The cost of adopting and developing AI is a significant barrier for hospitals. 54% of radiologists, who have not adopted AI, rank cost as their biggest barrier. It is estimated that the software licensing fee, hardware, and servers can cost at least $100 thousand to start working with AI in radiology.

Recommendation: Conduct a detailed financial analysis of the project to find out the expected ROI and try using innovative new techniques to make AI development in radiology more cost-effective. 

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

• Medical Image Annotation Tool
• Data Annotation / Labelling / Tagging / Classification Service
• Video Annotation Tools

Further reading

• 4 Ways AI is Revolutionizing The Radiology Sector
• Computer Vision in Radiology: Benefits & Challenges
• Top 7 Computer Vision Use Cases in Healthcare
• Top 4 Challenges of AI in Healthcare & How to Overcome Them