ChatGPT-4 accurately interprets thyroid, renal ultrasound images

ChatGPT-4 demonstrated high accuracy in analyzing and interpreting thyroid and renal ultrasound images in a study published March 19 in Radiology Advances. Researchers led by Laith Sultan, MD, from Children’s Hospital of Philadelphia reported that the large language model could accurately perform image segmentation and classify lesions on ultrasound as normal or abnormal. “These functionalities indicate the tool's potential to improve radiological workflows by pre-screening and categorizing ultrasound images,” Sultan told AuntMinnie.com. “This technology, if implemented in clinical practice, will have great potential in enhancing medical image interpretation and healthcare outcomes.” While ultrasound is a versatile tool that can be performed in various settings inside and outside of radiology, owing to the advent of point-of-care ultrasound (POCUS), it is still a user-dependent modality. AI algorithms, meanwhile, aim to aid radiologists and other medical practitioners who use ultrasound in image interpretation and lessen workloads. ChatGPT and other large language models continue to be explored for their clinical utility, with the researchers highlighting its potential and user-friendliness. Sultan and colleagues tested the performance of ChatGPT-4, the latest iteration of ChatGPT, in analyzing thyroid ultrasound images. In one test, the team analyzed a thyroid nodule on ultrasound imaging using the large language model. It requested ChatGPT-4 to find and mark...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Subspecialties Ultrasound Advanced Visualization Genitourinary Radiology Head and Neck Radiology Source Type: news