像病理学家一样思考：利用ChatGPT的肝胆肿瘤形态学诊断方法
Thinking like a pathologist: Morphologic approach to hepatobiliary tumors by ChatGPT
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影响因子:1.9
分区:医学4区 / 病理学3区
发表日期:2025 Jan 28
作者:
Thiyaphat Laohawetwanit, Sompon Apornvirat, Chutimon Namboonlue
DOI:
10.1093/ajcp/aqae087
摘要
本研究旨在评估ChatGPT在利用组织病理图像准确诊断肝胆肿瘤方面的有效性。通过比较GPT-4模型在提供相同图像集和两种不同输入提示下的诊断准确性。第一种提示采用模仿病理学家分析组织形态的方法，即形态学方法；第二种提示不包含此形态学分析功能。分析诊断准确率和一致性。共使用120张光镜照片，包括60张肝胆肿瘤和非肿瘤肝组织的图像。结果显示，采用形态学方法显著提高了AI的诊断准确性和一致性，特别是在识别肝细胞癌（平均准确率：62.0%对27.3%）、胆管腺瘤（10.7%对3.3%）和胆管癌（68.7%对16.0%）以及区分非肿瘤肝组织（77.3%对37.5%）方面（P值均≤.01）。此外，该模型在诊断一致性方面也优于未使用形态学分析的模型（κ值：0.46对0.27）。本研究强调将病理学家的诊断思路融入AI中，以提升医学诊断的准确性和一致性，展示了AI在复制专家诊断流程中的组织病理潜力。© 作者本人，2024年。由牛津大学出版社代表美国临床病理学会出版。版权所有。如需商业再利用，请联系 reprints@oup.com 获取再版和翻译权限。其他权限可通过本网站中的Permissions链接，利用RightsLink服务申请。详情请联系 journals.permissions@oup.com。
Abstract
This research aimed to evaluate the effectiveness of ChatGPT in accurately diagnosing hepatobiliary tumors using histopathologic images.The study compared the diagnostic accuracies of the GPT-4 model, providing the same set of images and 2 different input prompts. The first prompt, the morphologic approach, was designed to mimic pathologists' approach to analyzing tissue morphology. In contrast, the second prompt functioned without incorporating this morphologic analysis feature. Diagnostic accuracy and consistency were analyzed.A total of 120 photomicrographs, composed of 60 images of each hepatobiliary tumor and nonneoplastic liver tissue, were used. The findings revealed that the morphologic approach significantly enhanced the diagnostic accuracy and consistency of the artificial intelligence (AI). This version was particularly more accurate in identifying hepatocellular carcinoma (mean accuracy: 62.0% vs 27.3%), bile duct adenoma (10.7% vs 3.3%), and cholangiocarcinoma (68.7% vs 16.0%), as well as in distinguishing nonneoplastic liver tissues (77.3% vs 37.5%) (Ps ≤ .01). It also demonstrated higher diagnostic consistency than the other model without a morphologic analysis (κ: 0.46 vs 0.27).This research emphasizes the importance of incorporating pathologists' diagnostic approaches into AI to enhance accuracy and consistency in medical diagnostics. It mainly showcases the AI's histopathologic promise when replicating expert diagnostic processes.© The Author(s) 2024. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.