Variability of ChatGPT in Interpreting the Lexicon of ACR-TIRADS, EU-TIRADS, and K-TIRADS
Pierpaolo Trimboli, Amos Colombo, Lorenzo Ruinelli, Andrea Leoncini

TL;DR
This study examines how ChatGPT interprets different thyroid imaging reporting systems and finds variability in its responses.
Contribution
The study reveals variability in ChatGPT's interpretation when using different TIRADS terminology for thyroid nodules.
Findings
ChatGPT showed significant differences in interpreting ACR-TIRADS and EU-TIRADS terminology.
No significant differences were observed with K-TIRADS terminology.
Intraobserver agreement was moderate with K-TIRADS terminology.
Abstract
Background: There is an ongoing project to create an international Thyroid Imaging Reporting And Data System (I-TIRADS) to harmonize the terminology of guidelines for reporting thyroid ultrasonography. As artificial intelligence (AI) has been gaining increasing attention also in the thyroid field, achieving solid information about the consistency of AI in interpreting the TIRADS terminology is relevant before the I-TIRADS is published. The present study aimed to examine the issue of AI when interpreting the TIRADS terminology to describe thyroid nodules (TNs). Methods: Three TIRADSs from the USA (ACR-TIRADS), Europe (EU-TIRADS), and Asia (K-TIRADS) were considered. The most popular AI, such as ChatGPT, was tested. All possible combinations of terms of the three TIRADSs were performed. Results: 2592 cases were included. With the ACR-TIRADS lexicon, there was a slightly significant…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Radiomics and Machine Learning in Medical Imaging · Radiology practices and education
