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Large Language Models for Simplified Interventional Radiology Reports: A Comparative Analysis.

Elif Can¹, Wibke Uller¹, Katharina Vogt¹

¹Department of Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany (E.C., W.U., K.V., M.C.D.).

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Summary

This summary is machine-generated.

GPT-4 and Claude-3-Opus excel at simplifying interventional radiology reports. However, all large language models (LLMs) showed errors, necessitating further validation for clinical use.

Keywords:

Artificial Intelligence Interventional Radiology Large Language Model Patient Friendliness Structured Reporting

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Area of Science:

Artificial Intelligence
Medical Informatics
Radiology

Background:

Simplifying interventional radiology (IR) reports is crucial for patient comprehension and clinical decision-making.
Large language models (LLMs) offer potential for automating report simplification.

Purpose of the Study:

To quantitatively and qualitatively compare the performance of leading proprietary and open-source LLMs in simplifying IR reports.
To evaluate accuracy, clarity, clinical relevance, and error rates of simplified reports.

Main Methods:

109 IR reports were simplified using GPT-4, GPT-3.5 Turbo, Claude-3-Opus, Gemini Ultra, Mistral-7b, and Mistral-8x7b.
Qualitative assessment used a five-point Likert scale for accuracy, completeness, clarity, clinical relevance, naturalness, and error rates.
Quantitative readability was measured using Flesch Reading Ease, Flesch-Kincaid Grade Level, SMOG Index, and Dale-Chall Readability Score.

Main Results:

GPT-4 and Claude-3-Opus demonstrated superior qualitative performance, outperforming other models (p < 0.001).
GPT-4 showed the fewest content and trust-breaking errors, followed closely by Claude-3-Opus.
GPT-4 also outperformed other models in all quantitative readability metrics (p < 0.001).

Conclusions:

GPT-4 and Claude-3-Opus are the most effective LLMs for simplifying IR reports.
Despite superior performance, all models exhibited errors, including trust-breaking ones, requiring further refinement and validation before clinical deployment.