AI-Driven Diagnostic Imaging

If you have ever undergone an MRI, CT scan, X-ray, or other type of medical imaging, there’s a good chance AI has played some role in analyzing those images, especially in large hospitals. But probably not the way you may think it did.

This is because AI rarely works alone. Instead, radiologists frequently use AI to pre-screen images and highlight suspicious results. This reduces the time specialists spend reading images and the likelihood of errors while leaving more time for evaluating complex cases. 

AI is powerful because it works differently than humans. That starts with training. A radiologist might see 500,000 scans in a 30-year career. AI models train on tens of millions of images, including a greater diversity of demographics, scanner manufacturers, and hospital imaging protocols. It uses this data to learn the signatures and patterns associated with different conditions and diseases. As a result, it sees images differently than a radiologist might have learned them. 

AI brings other advantages to the table. AI looks at variations in pixels, the digital dots that form an image, unearthing patterns that would be invisible to humans. AI never gets tired and new systems make it possible to compare images with past scans as well as diagnostics from diverse types of equipment.

AI is also fast. This saves lives in emergency rooms, where seconds count when dealing with strokes and pulmonary embolisms. AI can analyze a CT brain scan and pinpoint a hemorrhage before a human has even opened the file. Its ability to analyze images on a pixel level also helps radiologists find tumors in their earliest stages. 

AI is not yet the standard of care in hospitals. Yet it is making inroads. Regulators have authorized more than 1,400 AI‑enabled medical devices, including over 1,100 tools used for imaging.