利用ChatGPT作为头颈部外科组织病理学鉴别诊断的可靠辅助

Sayyed Ourmazd Mohseni , Asal Saeid , Patrick Wong , Timothy Neal , Thomas Schlieve
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引用次数: 0

摘要

人工智能的兴起为医疗保健诊断工作流程提供了有希望的进步。在口腔颌面外科手术中,及时准确的组织病理学诊断对于制定有效的治疗方案至关重要。本研究检验了Chat生成预训练转换器(ChatGPT, OpenAI Inc., California)作为辅助提供者对四种常见颌面病理进行鉴别诊断的工具:成釉细胞瘤、鳞状细胞癌、粘液表皮样癌和多形性腺瘤。研究设计对200例去识别的组织病理学病例进行回顾性研究,平均分为四种诊断类别。每个病例包括临床总结和组织病理学图像,这些图像被输入到ChatGPT中以产生四种鉴别诊断。该研究使用卡方拟合优度检验评估正确诊断在鉴别列表中的包含和排名。结果atgpt诊断正确率为100%,排在首位(49.5%)、第二位(32.5%)、第三位(14.5%)和第四位(3.5%)。统计分析证实了患者对正确诊断排序较高的显著偏好(p <;0.001)。结论chatgpt对颌面部组织病理学的准确鉴别诊断具有较强的可靠性,82%的病例正确率排在前两位。这些结果突出了人工智能在增加诊断工作流程和提高效率方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilization of ChatGPT as a reliable aide for differential diagnosis of histopathology in head and neck surgery

Objectives

The rise of artificial intelligence offers promising advancements in diagnostic workflows in healthcare. In oral and maxillofacial surgery, timely and accurate histopathological diagnosis is crucial for effective treatment planning. This study examines Chat Generative Pretrained Transformer (ChatGPT, OpenAI Inc., California) as an aid to providers in generating differential diagnoses for four common maxillofacial pathologies: ameloblastoma, squamous cell carcinoma, mucoepidermoid carcinoma, and pleomorphic adenoma.

Study design

A retrospective study was conducted with 200 de-identified histopathological cases, evenly divided across the four diagnostic categories. Each case included clinical summaries and histopathological images, which were input into ChatGPT to generate four differential diagnoses. The study evaluated the inclusion and ranking of the correct diagnosis in the differential list using a chi-square goodness-of-fit test.

Results

ChatGPT included the correct diagnosis in all cases (100 %), ranking it first in 49.5 %, second in 32.5 %, third in 14.5 %, and fourth in 3.5 %. Statistical analysis confirmed a significant preference for higher ranking of correct diagnoses (p < 0.001).

Conclusion

ChatGPT shows strong reliability in generating accurate differential diagnoses for maxillofacial histopathology, ranking the correct diagnosis in the top two positions in 82 % of cases. These results highlight AI's potential to augment diagnostic workflows and enhance efficiency.
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