Artificial Intelligence (AI) has been making waves in various industries, and its impact on radiology is no exception. Integrating AI in radiology can significantly change how medical imaging is interpreted, diagnosed, and treated. With its ability to process vast amounts of data in a matter of seconds, AI is transforming the field of radiology from a manual, time-consuming process to a more efficient and accurate one.
The umbrella term AI refers to machine learning and deep learning, a branch of machine learning. Both use AI algorithms to create systems that make predictions or classifications based on input data.
The earliest records of Artificial Intelligence being employed in radiology go back to 1992, when it was utilized to identify microcalcifications in mammography. This application is more commonly referred to as computer-aided detection.
It wasn’t until the middle of the decade that radiologists began to view AI as a viable way to manage their workload and other everyday issues.
At the National Institutes of Health Clinical Center in Bethesda, Maryland, Ronald Summers, chief of the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, considers that deep-learning techniques have had a “democratizing influence” on the field since their emergence.
Ronald Summers says:
“The research is so much easier now.”
“You don’t have to have mathematical representations of disease; instead you feed in large amounts of data and the neural network that’s part of the deep-learning system learns the patterns on its own.”
Artificial Intelligence has led to the development of radionics, which involves transferring images, usually read qualitatively by radiologists, into numerical information.
Once the digital data is collected, it can be employed to instruct machine-learning algorithms to detect certain characteristics that may not be obvious to the human eye but could provide a diagnosis or prognosis.
AI applies to almost every step of a patient’s experience in the radiology department, and its use can be divided into three main areas.
Strategies For Improving Hospital Workflow Efficiency
Hospital operations are one of the first areas to consider regarding healthcare.
Summers went on say:
“There’s a lot of interest in AI applications to improve workflow.”
AI can help automate operational tasks, like determining if imaging is the right choice, organizing patient appointments, picking the correct examination protocols, and streamlining radiologists’ reporting processes.
Some of the leading players in radiology have quickly recognized the advantages these applications provide.
Madhuri Sebastian, the business leader of enterprise imaging at Philips, made a statement.
Madhuri Sebastian says:
“Workflow automation is a big opportunity and our customers recognize this need, especially with the staff shortages post-COVID.”
“We are focusing on improving operational efficiency with solutions like the Workflow Orchestration in our Image Management offering that streamlines the worklist for the radiologist and other solutions driving efficiency in the imaging workflow.”
Philips offers a range of products to cover the entire magnetic resonance (MR) process, like VitalEye, which can identify patient physiology and breathing movements, allowing for quick MR examination set-up in less than one minute. Moreover, there is MR Workspace for protocol selection powered by AI and SmartExam for examination preparation.
AI has the potential to significantly transform the field of radiology and bring about numerous benefits for both healthcare providers and patients. From improved accuracy in diagnoses to reduced wait times for test results, the integration of AI in radiology has the power to revolutionize medical imaging and patient care.
However, as with any new technology, there are concerns about its impact on the job market and the ethical considerations surrounding the use of AI in healthcare. It’s important to balance innovation and ethics to ensure that AI is used responsibly and beneficially. As AI advances and becomes more integrated into radiology, it will be interesting to see how it shapes and improves the field.