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

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

BackgroundThis 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−/...

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Bibliographic Details
Main Authors: Hao Hang, Liankai Yang, Zhongjie Wang, Zhebing Lin, Pengchong Li, Jiayue Zhu, Rang Liu, Shuai Pu, Xinghua Cheng
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Medicine
Subjects:
Electronic Health Records (EHRs)
clinician burnout
large language models (LLMs)
lung cancer
DeepSeek
GPT-3.5
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2025.1618858/full
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https://www.frontiersin.org/articles/10.3389/fmed.2025.1618858/full

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