# Intra- and Inter-Observer Reliability of ChatGPT-4o in Thyroid Nodule Ultrasound Feature Analysis Based on ACR TI-RADS: An Image-Based Study

## TL;DR

This study assesses how reliably ChatGPT-4o can analyze thyroid nodule ultrasound features compared to an expert using ACR TI-RADS guidelines.

## Contribution

The study evaluates ChatGPT-4o's intra- and inter-observer reliability in thyroid nodule ultrasound feature analysis using ACR TI-RADS.

## Key findings

- ChatGPT-4o showed moderate to substantial intra-observer agreement for echogenicity but low agreement for shape and margin.
- Inter-observer agreement between ChatGPT-4o and the expert was generally low, with the highest concordance for shape at 65%.
- Overall, ChatGPT-4o's performance varied significantly compared to expert assessments across ultrasound features.

## Abstract

Background/Objectives: Advances in large language models like ChatGPT-4o have extended their use to medical image analysis. Accurate assessment of thyroid nodule ultrasound features using ACR TI-RADS is crucial for clinical practice. This study aims to evaluate ChatGPT-4o’s intra-observer consistency and its agreement with an expert in analyzing these features from ultrasound image assessments based on ACR TI-RADS. Methods: This cross-sectional study used ultrasound images from 100 thyroid nodules collected prospectively between May 2019 and August 2021. Ultrasound images were analyzed by ChatGPT-4o, following ACR TI-RADS guidelines, to assess features of thyroid nodule including composition, echogenicity, shape, margin, and echogenic foci. The analysis was repeated after one week to evaluate intra-observer reliability. The ultrasound images were also analyzed by another ultrasound expert for the evaluation of inter-observer reliability. Agreement was measured using Cohen’s Kappa coefficient, and concordance rates were calculated based on alignment with the expert’s reference classifications. Results: Intra-observer agreement for ChatGPT-4o was moderate for composition (Kappa = 0.449) and echogenic foci (Kappa = 0.404), with substantial agreement for echogenicity (Kappa = 0.795). Agreement was notably low for shape (Kappa = −0.051) and margin (Kappa = 0.154). Inter-observer agreement between ChatGPT-4o and the expert was generally low, with Kappa values ranging from −0.006 to 0.238, the highest being for echogenic foci. Overall concordance rates between ChatGPT-4o and expert evaluations ranged from 46.6% to 48.2%, with the highest for shape (65%) and the lowest for echogenicity (29%). Conclusions: ChatGPT-4o showed favorable consistency in assessing some thyroid nodule features in intra-observer analysis, but notable variability in others. Inter-observer comparisons with expert evaluations revealed generally low agreement across all features, despite acceptable concordance for certain imaging characteristics. While promising for specific ultrasound features, ChatGPT-4o’s consistency and accuracy still vary significantly compared to expert assessments.