ArticleFrontiers in medicine2025
Comparative analysis of accuracy and completeness in standardized database generation for complex multilingual lung cancer pathological reports: large language model-based assisted diagnosis system vs. DeepSeek, GPT-3.5, and healthcare professionals with varied professional titles, with task load variation assessment among medical staff.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Benchmark evaluation of large language models for clinical decision support in headache management.Journal of oral & facial pain and headache · 2026Article
- [Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- Artificial intelligence-assisted medical coding and DRG management: current applications, challenges, and future perspectives.Frontiers in medicine · 2026Review
- Generative artificial intelligence in lung cancer care: current applications, challenges, and future directions.Frontiers in oncology · 2026Review
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9 authors.
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Abstract
Background: This study evaluates how AI enhances EHR efficiency by comparing a lung cancer-specific LLM with general-purpose models (DeepSeek, GPT-3.5) and clinicians across expertise levels, assessing accuracy and completeness in complex lung cancer pathology documentation and task load changes pre-/post-AI implementation. Methods: This study analyzed 300 lung cancer cases (Shanghai Chest Hospital) and 60 TCGA cases, split into training/validation/test sets. Ten clinicians (varying expertise) and three AI models (GPT-3.5, DeepSeek, lung cancer-specific LLM) generated pathology reports. Accuracy/completeness were evaluated against LeapFrog/Joint Commission/ACS standards (non-parametric tests); task load changes pre/post-AI implementation were assessed via NASA-TLX (paired Results: This study analyzed 1,390 structured pathology databases: 1,300 from 100 Chinese cases (generated by 10 clinicians and three LLMs) and 90 from 30 TCGA English reports. The lung cancer-specific LLM outperformed nurses, residents, interns, and general AI models (DeepSeek, GPT-3.5) in lesion/lymph node analysis and pathology extraction for Chinese records ( Conclusion: The fine-tuned lung cancer LLM outperformed non-chief physicians and general LLMs in accuracy/completeness, significantly reduced medical staff workload (
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