Using artificial intelligence in radiology clinical practice
Science & Technology
Using Artificial Intelligence in Radiology Clinical Practice
Artificial intelligence (AI) in healthcare might sound like a futuristic concept, but it is already complementing the expertise of doctors today. Radiology was one of the first areas to see considerable AI applications. This is largely because AI thrives on data, and radiology has an abundance of digital data ready to be leveraged by AI.
Dr. Bradley Erickson, Director of Mayo Clinic's AI Lab, explains that in radiology, machine learning is utilized to handle some of the more time-consuming tasks. "It's actually doing a lot of the more mundane work like tracing tumors, tracing structures, measuring the amount of fat and muscle in body CTs," he says. If a computer can manage these initial tasks, it significantly aids the radiologists.
In addition to these basic tasks, the diagnostic capabilities of AI are particularly appealing. AI helps in detecting intracranial aneurysms, intracranial stroke, and pulmonary embolism. It is also used to identify some potential molecular markers.
While imaging-related AI has seen significant advancements, Dr. Bavik Patel, Director of AI at Mayo Clinic Arizona, advocates for the next step: expanding AI applications into preventive health. He suggests a shift from pipeline to platform thinking. For instance, there is now an AI model that can incidentally flag high levels of coronary artery calcium, signaling a high risk for heart attacks or strokes within five or ten years. Previously undetected risks can now be identified, and patients who might not be seeing a primary care physician or taking necessary medications can be proactively managed.
Beyond radiology, there are broad AI applications spreading into other areas of the clinic, including cardiology and pathology.
Keywords
- Artificial Intelligence (AI)
- Healthcare
- Radiology
- Machine Learning
- Diagnostic Capabilities
- Preventive Health
- Coronary Artery Calcium
- Tumor Tracing
- Molecular Markers
- Mayo Clinic
FAQ
Q: What is the role of AI in radiology?
A: AI primarily handles time-consuming tasks such as tracing tumors and structures and measuring fat and muscle in body CTs. It also aids in diagnostics by detecting conditions like intracranial aneurysms and strokes.
Q: Why is radiology a leading area for AI applications?
A: Radiology has an abundance of digital data, which is ideal for AI, as machine learning relies on large datasets to function effectively.
Q: How is AI being used in preventive health measures?
A: AI models can now identify potential risks, such as high levels of coronary artery calcium, and suggest early interventions. This can help in managing conditions before they turn into severe health issues.
Q: Apart from radiology, where else is AI being applied in healthcare?
A: AI applications are expanding into other areas, including cardiology and pathology, offering broad potential within clinical practice.
Q: What mindset shift is needed for the future of AI in healthcare?
A: There needs to be a shift from pipeline thinking, which focuses on individual tasks, to platform thinking, which integrates AI into a broader healthcare strategy focusing on preventive measures and comprehensive patient management.