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Large language models (LLMs) show promise in generating complete and correct radiology report impressions for lung cancer. However, current LLMs lack the conciseness and verisimilitude needed to fully replace radiologist-authored reports.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Natural Language Processing in Healthcare
  • Radiology Report Generation

Background:

  • Large language models (LLMs) demonstrate significant text generation capabilities.
  • The efficacy of LLMs in summarizing radiology report impressions is not well-established.
  • This is particularly relevant for lung cancer diagnosis and reporting.

Purpose of the Study:

  • To evaluate the performance of nine LLMs in summarizing Chinese radiology report impressions for lung cancer.
  • The study assessed models including Tongyi Qianwen, ERNIE Bot, ChatGPT, Bard, Claude, Baichuan, ChatGLM, HuatuoGPT, and ChatGLM-Med.
  • The focus was on impressions derived from computed tomography (CT), positron emission tomography (PET)-CT, and ultrasound (US) reports.

Main Methods:

  • A dataset of 100 Chinese radiology reports for each modality (CT, PET-CT, US) from patients with suspected or confirmed lung cancer was collected.
  • Zero-shot, one-shot, and three-shot prompting strategies were employed to generate impressions.
  • Generated impressions were evaluated using automatic quantitative metrics and human assessments by thoracic surgeons and a radiologist based on completeness, correctness, conciseness, verisimilitude, and replaceability.

Main Results:

  • ERNIE Bot, Tongyi Qianwen, and Claude showed top performance in CT, PET-CT, and US impression generation, respectively, in automatic evaluations.
  • Human evaluation indicated ERNIE Bot excelled in CT impressions (conciseness, verisimilitude, replaceability).
  • Tongyi Qianwen led in PET-CT, and Claude in US impressions, though overall generated impressions lacked conciseness and verisimilitude compared to radiologist originals.

Conclusions:

  • Current LLMs can generate radiology impressions with high completeness and correctness.
  • However, limitations in conciseness and verisimilitude prevent LLM-generated impressions from fully replacing those authored by radiologists.
  • Further advancements are needed to bridge the gap between AI-generated and expert-written radiology reports.