ChatGPT identifies incidental CT findings

ChatGPT-4 performs well in identifying incidental findings on CT via a process called single-shot learning, according to Canadian research published January 10 in the American Journal of Roentgenology. A team led by Rajesh Bhayana, MD, from Toronto General Hospital in Canada found that the large language model achieved a perfect F1 score for incidental adrenal nodules and highlighted that their results show that such models can be applied flexibly in medical settings. “Since incidental findings are commonly mismanaged, automatic identification in reports could improve management by increasing visibility or partially automating workup,” the Bhayana team wrote. While it’s common for clinically important incidental imaging findings to be reported, they can be overlooked or not managed properly. Large language models such as ChatGPT have shown high performance in completing tasks after training with few examples, also known as few-shot learning. Similarly, previous studies have explored their promise after training with just one example, known as single-shot learning. Bhayana and colleagues highlighted that as these models become more implemented into electronic medical record (EMR) software, they could be used more flexibly than traditional natural language processing models. For their study, the researchers tested ChatGPT-4’s performance with single-shot learning for identifying incidental findings in radiology reports, which contain medical jargon. They randomly ...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: CT Advanced Visualization Source Type: news