Benefits of AI in Radiology:

  • Early detection of cancers
  • Improved prioritization
  • More accurate classifications
  • Optimized radiology dosing
  • Reduced radiation exposure
  • Enhanced imaging quality
  • Optimized imaging analysis
  • Reduced medical errors
  • Improved satisfaction
  • Faster and better diagnosis
  • Improved access to care
  • Improved reporting
  • Less work for a human
  • Generating 3D models
  • Quicker Results


What is Artificial Intelligence (AI) in radiology?

Just like in other areas of our lives, Artificial Intelligence (AI) is a machine’s ability to mimic the cognitive skills of humans. AI is a collection of algorithms and machine learning tools coupled with sophisticated neural networks and systems that can recognize patterns to aid radiologists in patient care. AI is not a replacement for human beings, but it can rapidly analyze images and data registries. For example, AI can sort through patient histories for relevant findings, achieving a clearer picture of a patient’s condition in minutes.

How is AI used in radiology

AI is used to improve the patient experience, the staff experience and the health of the population as well as reduce the cost of healthcare. AI can be predictive so care can be provided faster and with better outcomes, generally resulting in less time in the hospital. The staff experiences a more integrated workflow, with improved communication and less burnout. Resource waste can be reduced in the ER and in other areas of care using this technology.  
AI is shifting radiology from an active to a proactive science. The population benefits from information aggregated nationally improve diagnostic accuracy so the right treatment can be provided at the right time. 

When was AI introduced in radiology?

In 1992, AI was first applied in radiology to detect microcalcifications in mammography. As imaging technologies have gained in sophistication, they have also increased in volume. Due to this volume, AI has expanded into all areas of radiology. In 2017, the FDA approved its first AI-based algorithm.

Why is AI in medical imaging important?

AI is increasingly important in radiology because it becomes that extra team member. It can sort through tremendous amounts of unstructured visual information (CT scans, X-rays, MRIs, etc.) round the clock. AI filters into workflows and can be customized to meet specific radiology requirements. The sophisticated algorithms can support clinician decision making. 
Not only does AI support radiologists as a second pair of hands and eyes, but AI also provides time for the radiologist to focus on complex cases that require individualized attention. 

How many radiologists use AI?

A 2020 survey conducted by the Journal of the American College of Radiology found that of 1,427 radiologists, one third reported using AI. However, the radiology market is moving rapidly. According to McKinsey & Co., 93% of key decision makers are expected to adopt AI imaging solutions within the next five years.

Why is AI used in radiology?

AI tackles two of medical imaging’s biggest hurdles: time and an overload of image information. AI takes on the laborious process of analyzing, documenting and evaluating images quickly to provide prompt care to patients, all while reducing medical errors. Additionally, AI in imaging allows radiologists to inspect medical imaging results as one source of data to create a single picture of a patient’s health condition.

How does AI affect radiology?

Using AI-based applications, physicians can make alternative diagnoses or see anatomical structures much sharper and finer than with previous technologies. Having AI workflows in place, radiologists are free to focus on expertise-based tasks like more personalized care or complex patient conditions. For example, it can take a doctor many hours to help a patient with chest pain—from sorting through the patient’s medical history and medications to the CT scans, labs, etc. AI programs can suggest possible diagnoses in minutes. 
Benefits include:

  • Better diagnosis
  • Prioritization of care
  • Less time in the hospital
  • Detection of life-threatening illnesses quickly
  • Scalability
  • More transparency
Will AI take over radiology?

Artificial intelligence is not taking over radiology, but it is providing additional learning and systems that arm radiologists with more information. It is a collaborative tool that can enhance, support and streamline the process, reducing the diagnosis burden and improving workflow efficiency. 
The longer AI is used, the deeper the learning and radiologists can treat more patients effectively and efficiently. Human discernment paired with deep learning of AI is the future of radiology. Radiology could become one of the most creative medical specialties for those seeking to problem-solve. 

What software do radiologists use?

Picture Archiving and Communication Systems (PACS) are the backbone of modern radiology, including image capture, management, transfer, distribution and storage. PACS assist radiologists and their health systems from initial image to diagnosis. While technology continues to evolve and change, so do PACS. PACS are robust solution sets intended to house, recall and archive medical imaging file formats. In their best iteration, PACS are intuitive, responsive and user-friendly. Yet, they are also flexible and customizable to the end user. When radiologists and system administrators evaluate software, KLAS rankings are an important consideration.

What is machine learning in radiology?

Machine learning means a computer improves its performance on future tasks based on information from past tasks. Essentially, computers learn from data accumulation. Machine learning algorithms evolve with increasing exposure to data. Computers learn from examples to understand, interpret and label diagnostic images. Complex patterns can be identified by machines and aid radiologists in evaluating data reports from CT, MRI and PET images. 
Deep learning is a subset of machine learning used most in radiology that is multiple layers of algorithms interconnected with hierarchies of importance for the most meaningful data. 

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