Keywords: Language Models, Language Models
Motivation: The performance of privacy-preserving LLMs for the generation of impressions in the radiology reports in multi-centers has not yet been investigated.
Goal(s): To develop privacy-preserving LLMs that generates the Impressions from Findings, and compare the performance with a public LLM (GPT4-turbo) on data from two centers.
Approach: Four privacy-preserving LLMs, including ChatGLM-6B, LLaMA2-Chinese-7B, Qwen1.5-7B and Baichuan2-7B, were finetuned. GPT4-turbo’s output was also optimized by prompt engineering. An automatic method for evaluating the similarities between impression items was proposed.
Results: Privacy-preserving LLMs offer enhanced accuracy in generating impressions, but performance varies across centers, highlighting their potential as a quality improvement tool under expert review.
Impact: we find that while LLMs can correct some diagnostic errors, they also introduce inaccuracies, underscoring the critical role of radiologist oversight. We believe these findings demonstrate the potential of LLMs as a valuable quality improvement tool in radiology.
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