What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Evaluating Large Language Model’s Potential to Optimize Structured MRI Reporting in Breast Cancer Diagnosis

Key Points

The high sensitivity of the model indicates effective capture of positive features in breast MRI reports.
ROC analysis showed diagnostic performance comparable to physician-annotated tests, especially for HR and HER2.
Using OpenAI's text-davinci-003 model on reports from 237 patients helped evaluate its clinical application feasibility.
The study highlights AI's potential to enhance diagnostic accuracy in multilingual and resource-limited settings.

Abstract

Motivation: Motivated by the need for precise breast cancer diagnosis, this study investigates whether LLMs, like ChatGPT, can optimize MRI reporting in line with BI-RADS guidelines. Goal(s): We aimed to convert clinical reports into structured data and evaluate the feasibility for clinical application. Approach: Using OpenAI's text-davinci-003 model on reports from 237 patients, we found Kappa values indicated variable agreement. Results: The high sensitivity of the model suggests effective capture of positive features. ROC analysis showed that GPT's diagnostic performance was comparable to physician-annotated tests, particularly for HR and HER2. Impact: This study highlights the promising role of AI in radiology, potentially enhancing diagnostic accuracy and supplementing radiological expertise, especially in multilingual and resource-limited settings.

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