Robert Harris the use of a generative AI model for chest X-rays in a large radiology practice

Generative artificial intelligence (AI) is poised to reshape chest X-ray interpretation by moving beyond traditional algorithms that detect a single abnormality, to models capable of generating an entire radiology report from medical images. While the technology remains investigational in many practices, researchers say it has the potential to improve diagnostic accuracy, streamline AI development and strengthen radiology quality assurance.

At the Radiological Society of North America (RSNA) 2025 annual meeting, Robert Harris, PhD, a machine learning engineer at Virtual Radiologic (vRad), spoke with Radiology Business about generative AI in the above video interview. He outlined his team's work developing and evaluating a generative chest X-ray machine learning model for use within one of the nation's largest teleradiology practices.

"These types of generative models have seen an uptick in their use since the release of ChatGPT," Harris said. "Instead of targeting single pathologies like many traditional AI models, they generate an entire radiology report from the input images."

Unlike earlier computer vision algorithms that were trained to identify individual findings such as pneumonia or lung nodules, generative models use radiologists' dictated reports as the ground truth during training. That allows developers to leverage existing clinical reports rather than manually labeling thousands of images or creating detailed image segmentations.