Top 5 Use Cases for Artificial Intelligence in Medical Imaging
Artificial intelligence and machine learning have captivate the healthcare industry as these innovative analytics strategies become more accurate and applicable to a variety of tasks.
AI is increasingly helping to uncover hidden insights into clinical decision-making, connect patients with resources for self-management, and extract meaning from previously inaccessible, unstructured data assets.
Medical imaging data is one of the richest sources of information about patients, and often one of the most complex.
With megapixel upon megapixel of data packed into the results from X-rays, CAT scans, MRIs, and other testing modalities, combing through extremely high-resolution images can be challenging even for the most experienced clinical professional.
Artificial intelligence has already proven that it may be a valuable ally for radiologists and pathologists looking to accelerate their productivity and potentially improve their accuracy.
Multiple studies have indicated that AI tools can perform just as well, if not better, than human clinicians at identifying features in images quickly and precisely.
Far from being a matter of concern for the American College of Radiology, the advent of AI as a companion for diagnosticians is a positive development, leaders of the society said.
In order to foster standardized, safe, and effective AI for clinical decision support and diagnostics, the American College of Radiology Data Science Institute (ACR DSI) has released a number of high-value use cases for artificial intelligence in medical imaging, which will be continuously updated as new opportunities present themselves.
“The ACR DSI use cases present a pathway to help AI developers solve health care problems in a comprehensive way that turns concepts for AI solutions into safe and effective tools to help radiologists provide better care for our patients,” said Bibb Allen Jr., MD, FACR, ACR, Chief Medical Officer at DSI.
As part of the ACR DSI Technology Oriented Use Cases in Health Care-AI (TOUCH-AI) Framework, theuse cases offer real-world scenarios in which AI tools can supplement and enhance the process of using medical images to deliver high-quality patient care across a wide variety of diseases and organ groups.
What are the top five use cases for artificial intelligence in the imaging world, and how can AI tools alter workflows to improve the detection and diagnosis of potentially fatal conditions?
Measuring the various structures of the heart can reveal an individual’s risk for cardiovascular diseases or identify problems that may need to be addressed through surgery or pharmacological management.
Automating the detection of abnormalities in commonly-ordered imaging tests, such as chest x-rays, could lead to quicker decision-making and fewer diagnostic errors.
For example, when a patient enters the emergency department with a complaint such as shortness of breath, “the chest radiograph is often the first imaging study that is available,” ACR DSI says.
“It can be used as a quick initial screening tool for cardiomegaly, which in and of itself can be used as a marker for heart disease. A quick visual assessment by a radiologist is sometimes inaccurate.”
Using artificial intelligence to identify left atrial enlargement from chest x-rays could rule out other cardiac or pulmonary problems and help providers target appropriate treatments for patients.
Similar AI tools could be used to automate other measurement tasks, such as aortic valve analysis, carina angle measurement, and pulmonary artery diameter.
Applying AI to imaging data may also help to identify thickening of certain muscle structures, such as the left ventricle wall, or monitor changes in blood flow through the heart and associated arteries.
