In recent years, there has been much talk of the potential for large language models to improve radiology workflows.

Nina Kottler, MD, associate CMO for clinical AI at Radiology Partners, explains the movement toward greater regulation of artificial intelligence and the need to test for bias. 

The healthcare market analysis firm Signify Research released a list of predictions in radiology its analysts expect to see in 2025. 

Recommendations for additional imaging are routinely included in radiology reports but are sometimes overlooked or not communicated in a timely manner. Experts believe large language models can help address these lapses in care. 

Researchers estimate that it could cost less than $0.01 per report to use the large language model as a radiology report proofreader. 

Radiology researchers have developed and validated an automated program for tracking incidental imaging findings. The system facilitates communications between radiologists, patients and primary care providers whenever such findings turn up.  

In a new clinical study, the model was able to maintain its performance on scans completed on different types of CT equipment across multiple medical centers.  

Just like radiologists, the performance of large language models improves when given proper context.