Performance analysis of large language models in multi-disease detection from chest computed tomography reports: a comparative study Article Swipe
Peng Luo
,
Chaofan Fan
,
Anghua Li
,
Tong Jiang
,
Aimin Jiang
,
Qi Chang
,
Wenyi Gan
,
Lingxuan Zhu
,
Weiming Mou
,
Dongqiang Zeng
,
Bufu Tang
,
Mingjia Xiao
,
Guangdi Chu
,
Zhenyu Liang
,
Junyi Shen
,
Zaoqu Liu
,
Ting Wei
,
Quan Cheng
,
Anqi Lin
,
Xin Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1097/js9.0000000000002582
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1097/js9.0000000000002582
Background: Computed tomography (CT) is widely acknowledged as the gold standard for diagnosing thoracic diseases. However, the accuracy of interpretation significantly depends on radiologists’ expertise. Large language models (LLMs) have shown considerable promise in various medical applications, particularly in radiology. This study aims to assess the performance of leading LLMs in analyzing unstructured chest CT reports and to examine how different questioning methodologies and fine-tuning strategies influence their effectiveness in enhancing chest CT diagnosis. Materials and methods: This retrospective analysis evaluated 13 489 chest CT reports encompassing 13 common thoracic conditions across pulmonary, cardiovascular, pleural, and upper abdominal systems. Five LLMs (Claude-3.5-Sonnet, GPT-4, GPT-3.5-Turbo, Gemini-Pro, Qwen-Max) were assessed using dual questioning methodologies: multiple-choice and open-ended. Radiologist-curated datasets underwent rigorous preprocessing, including RadLex terminology standardization, multi-step diagnostic validation, and exclusion of ambiguous cases. Model performance was quantified via Subjective Answer Accuracy Rate (SAAR), Reference Answer Accuracy Rate (RAAR), and Area Under the Receiver Operating Characteristic (ROC) Curve analysis. GPT-3.5-Turbo underwent fine-tuning (100 iterations with one training epoch) on 200 high-performing cases to enhance diagnostic precision for initially misclassified conditions. Results: GPT-4 demonstrated superior performance with the highest RAAR of 75.1% in multiple-choice questioning, followed by Qwen-Max (66.0%) and Claude-3.5 (63.5%), significantly outperforming GPT-3.5-Turbo (41.8%) and Gemini-Pro (40.8%) across the entire patient cohort. Multiple-choice questioning consistently improved both RAAR and SAAR for all models compared to open-ended questioning, with RAAR consistently surpassing SAAR. Model performance demonstrated notable variations across different diseases and organ conditions. Notably, fine-tuning substantially enhanced the performance of GPT-3.5-Turbo, which initially exhibited suboptimal results in most scenarios. Conclusion: This study demonstrated that general-purpose LLMs can effectively interpret chest CT reports, with performance varying significantly across models depending on the questioning methodology and fine-tuning approaches employed. For surgical practice, these findings provided evidence-based guidance for selecting appropriate AI tools to enhance preoperative planning, particularly for thoracic procedures. The integration of optimized LLMs into surgical workflows may improve decision-making efficiency, risk stratification, and diagnostic speed, potentially contributing to better surgical outcomes through more accurate preoperative assessment.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1097/js9.0000000000002582
- OA Status
- diamond
- Cited By
- 2
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411209557
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411209557Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1097/js9.0000000000002582Digital Object Identifier
- Title
-
Performance analysis of large language models in multi-disease detection from chest computed tomography reports: a comparative studyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-05Full publication date if available
- Authors
-
Peng Luo, Chaofan Fan, Anghua Li, Tong Jiang, Aimin Jiang, Qi Chang, Wenyi Gan, Lingxuan Zhu, Weiming Mou, Dongqiang Zeng, Bufu Tang, Mingjia Xiao, Guangdi Chu, Zhenyu Liang, Junyi Shen, Zaoqu Liu, Ting Wei, Quan Cheng, Anqi Lin, Xin ChenList of authors in order
- Landing page
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https://doi.org/10.1097/js9.0000000000002582Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1097/js9.0000000000002582Direct OA link when available
- Concepts
-
Medicine, Computed tomography, Radiology, Medical physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
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57Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5087942504, https://openalex.org/A5100741537, https://openalex.org/A5082582836 |
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| corresponding_institution_ids | https://openalex.org/I111599522, https://openalex.org/I111950717, https://openalex.org/I139660479, https://openalex.org/I204512498, https://openalex.org/I4210120929, https://openalex.org/I4210159865, https://openalex.org/I58200834, https://openalex.org/I83519826 |
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