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Top 6 Radiology AI Use Cases in 2024

Top 6 Radiology AI Use Cases in 2024Top 6 Radiology AI Use Cases in 2024

The ability of healthcare professionals to analyze and process information is limited. At the point where the human mind and eyes fall short, digital solutions pave the way. AI is making the healthcare sector smarter and more efficient, and one of the major areas AI has impacted in the healthcare industry is radiology.

As radiologists realize the benefits of artificial intelligence and machine learning, they invest more in smart medical imagining technology. The global radiology or medical imaging marketplace is rapidly growing, according to a recent study by the National Library of Medicine. The report also suggests radiologists and administrators to improve their knowledge of the current offerings and factors important for the implementation of AI-powered radiology solutions before initiating any investments.

Therefore, this article explores:

  • Why AI is important for the radiology sector
  • 6 applications and use cases of AI in the radiology field
  • Some recommendations and considerations while investing in the technology.

Why is AI important for the radiology sector?

AI has the potential to revolutionize radiology, a field that already relies heavily on technology. AI-based radiology systems can automate processes and provide more accurate diagnostics than human analysts in some cases. With AI-powered medical imaging solutions, radiologists can reduce their workload and improve accuracy by reducing false positives, false negatives, or missed detections. AI radiology solutions are also increasingly capable of detecting subtle patterns that would be difficult for human radiologists to identify.

However, business leaders in the healthcare sector need to understand that the role of AI-powered tools is not to provide a final diagnosis and replace radiologists. Business and healthcare professionals need to approach this technology as a tool to complement and enhance medical imaging processes and support radiologists.

The AI radiology market offers solutions in the following 4 areas:

A pie chart illustrating the 4 areas of ai radiology offerings. repetitive is 20%, diagnostic is 49%, quantitative is 30% and explorative is only 1 %.
Source: NCBI

Top 6 applications/use cases of AI in radiology

1. Breast cancer detection

Breast cancer is the most common type of cancer in the world as of 2021. Mammography is the most used screening method for breast cancer detection in radiology practice. However, 40% of cancers are missed by doctors while evaluating mammograms. 

Computer vision (CV) technology is taking the healthcare sector to new levels. AI-enabled computer vision and medical imaging systems can accurately analyze mammograms to detect cancers that are not visible to the human eye. The machine learning models are trained with medical image data to provide a more accurate analysis of breast masses, breast density, and mass segmentation, and provide better cancer risk assessments. However, current machine learning algorithms and models are not mature enough to detect all types of cancers. There are some cancers that are found by doctors and missed by the ML model. 

Therefore, while implementing this technology in a healthcare facility, managers should not try to replace doctors but instead implement this technology to complement and assist doctors in enabling computer aided detection of breast cancer.

Research by Google and Northwestern Medicine found similar results

2. Tumor classification

Classifying tumors is an important part of tumor diagnosis and treatment since it helps doctors and surgeons decide which course of action to take. However, it is a time-consuming process. AI-powered medical imaging tools can be used to analyze medical images, such as MRI and CT scans, to detect tumors and other abnormal cell structures accurately and quickly. These AI systems also have the ability to detect tumors even in complex brain imaging scenarios.

According to a recent study, using advanced imaging and AI, tumor classification can be performed in less than 3 minutes (three times faster than manual), resulting in faster and more accurate diagnoses.

Another study conducted in the UK found AI and advanced imaging effective in diagnosing brain tumors in children with non-invasive methods.

See how it works:

3. Finding hidden fractures

Another application of AI in radiology is detecting bone fractures that are difficult to see with the human eye. AI-powered medical imaging tools can help detect hidden bone fractures by reducing noise and providing more accurate representations of the radiological images.

Machine learning algorithms can be trained to recognize patterns in radiological images that may signify a fracture or other anomaly, and then alert medical professionals to further investigate. AI radiology tools are also capable of identifying subtle characteristics of a fracture that may be otherwise undetected by radiology professionals.

A recent study has shown that software using AI algorithms can accurately detect wrist fractures that are not visible to the human eye.

AI-enabled medical imaging systems can also detect hip fractures, which are usually hard to spot due to the soft tissues in a hip. However, such solutions can only enable computer aided detection of anomalies and not work autonomously.

Image showing an AI system detecting fractures on x-ray images with box labels.
Source: datasciencedistrict

4. Detecting neurological abnormalities

AI can also be used to detect neurological disorders and illnesses, such as dementia, Alzheimer’s, Parkinson’s, etc. A recent study introduced an AI tool based on a convolutional neural network that can recognize speech patterns in Alzheimer’s patients’ conversations to make diagnoses with 95% accuracy. Since the language pattern of an Alzheimer’s patient changes, the AI model can detect that.

Watch how this company uses AI to analyze patient data and make early diagnoses of Alzheimer’s and dementia. The application then refers the patient to a general physician to make further assessments and complete the diagnosis.

5. Automated lesion detection

Lesions are irregularities in radiological images that could indicate a problem. AI and machine learning algorithms are now being used to detect lesions faster and more accurately. With automated lesion detection powered by AI systems such as deep learning networks, medical professionals are able to detect lesions from radiology scans earlier which leads to improved diagnostic accuracy and early intervention for patients in need of treatment options sooner rather than later.

For instance, the University of California San Francisco Medical Center (UCSF) is extensively researching the usage of AI tools in the radiology sector. One of their studies identified that AI-powered automated lesion detection tools are not only helping radiologists in detecting lesions more accurately but also suggesting the most appropriate imaging exam for the patient.

6. Lung cancer screening

The use of radiology AI is becoming increasingly important for early lung cancer screening. AI-powered medical imaging tools can help to detect subtle signs and abnormalities that would otherwise go undetected by radiological methods alone. This improved detection can lead to earlier diagnosis, which can improve survival rates among those with lung cancer. Studies show that such computer-aided detection systems can help reduce the labor work in the following lung cancer diagnosis processes:

  • Low-dose computed tomography (LDCT)
  • Chest radiographs (CXR)
  • Pathology slide reading
  • Nodule detection

Our recommendations while investing in radiology AI

While investing in AI-powered solutions to improve your radiology facility, the following points can be considered:

  • Consider the adequacy of existing radiology processes and how incorporating AI can help improve them. This involves mapping out how such solutions can be integrated into the clinical workflow.
  • Medical training data sets are difficult to find. Therefore, it is important to consider if enough digital data is available to implement the system and which ones could benefit from the use of AI/ML algorithms.
  • Identify experts or consultants in the radiology field who can provide valuable insight into the best ways to implement AI-powered radiology systems.
  • Ensure that your radiology facility is ready to invest in the hardware and technical expertise needed for AI-powered medical imaging.
  • Since AI-powered radiology solutions can not operate autonomously, ensure that the staff in your radiology facility is trained and equipped to leverage such tools.
  • Legal and ethical issues can be a barrier while implementing AI-powered solutions in the healthcare sector. Therefore we recommend going through the country specific regulations regarding using AI-powered solutions in a radiology facility (such as data collection regulations), to avoid lawsuits related to legal and ethical issues.

To elevate and ensure the quality of your data annotation, check out our data-driven list of tools and services:

Further reading

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Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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