Image-to-text strategy helps ChatGPT read thyroid ultrasound images

Large language models integrated with image-to-text approaches could potentially improve diagnostic thyroid ultrasound interpretation, according to research published March 12 in Radiology. A team led by Li-Da Chen, MD, PhD, from First Affiliated Hospital of Sun Yat-sen University in Guangzhou found that ChatGPT 4.0 had the highest consistency and diagnostic accuracy when compared with Google Bard and ChatGPT 3.5 when it came to interpreting ultrasound images of thyroid nodules. Also, the image-to-text large language model strategy showed comparable performance to that of human large language model interaction involving two senior readers and one junior reader. “The results indicate that combining image-to-text models and large language models could advance medical imaging and diagnostics research and practice, informing secure deployment for enhanced clinical decision-making,” the Chen team wrote. While previous studies have explored the potential of large language models in medical imaging interpretation, the researchers noted a lack of studies investigating the feasibility of the models in handling reasoning questions tied to medical diagnosis. Chen and colleagues studied the viability of leveraging three publicly available models in this area: ChatGPT 4.0, ChatGPT 3.5, and Bard. The researchers explored how the models could improve consistency and diagnostic accuracy in medical imaging based on standardized reporting, with pathology as the reference standard. The...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Ultrasound Source Type: news