Shahid Fahad, Hsu Min-Huei, Chang Yung-Chun, Jian Wen-Shan
Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
Department of Neurosurgery, Shuang-Ho Hospital-Taipei Medical University, Taipei, Taiwan.
J Med Syst. 2025 Mar 13;49(1):36. doi: 10.1007/s10916-025-02167-2.
Manually converting unstructured text pathology reports into structured pathology reports is very time-consuming and prone to errors. This study demonstrates the transformative potential of generative AI in automating the analysis of free-text pathology reports. Employing the ChatGPT Large Language Model within a Streamlit web application, we automated the extraction and structuring of information from 33 unstructured breast cancer pathology reports from Taipei Medical University Hospital. Achieving a 99.61% accuracy rate, the AI system notably reduced the processing time compared to traditional methods. This not only underscores the efficacy of AI in converting unstructured medical text into structured data but also highlights its potential to enhance the efficiency and reliability of medical text analysis. However, this study is limited to breast cancer pathology reports and was conducted using data obtained from hospitals associated with a single institution. In the future, we plan to expand the scope of this research to include pathology reports for other cancer types incrementally and conduct external validation to further substantiate the robustness and generalizability of the proposed system. Through this technological integration, we aimed to substantiate the capabilities of generative AI in improving both the speed and reliability of data processing. The outcomes of this study affirm that generative AI can significantly transform the handling of pathology reports, promising substantial advancements in biomedical research by facilitating the structured analysis of complex medical data.
将非结构化文本病理报告手动转换为结构化病理报告非常耗时且容易出错。本研究展示了生成式人工智能在自动化分析自由文本病理报告方面的变革潜力。我们在一个Streamlit网络应用程序中使用ChatGPT大语言模型，实现了从台北医学大学医院的33份非结构化乳腺癌病理报告中自动提取和构建信息。该人工智能系统的准确率达到99.61%，与传统方法相比，显著缩短了处理时间。这不仅强调了人工智能在将非结构化医学文本转换为结构化数据方面的有效性，还突出了其提高医学文本分析效率和可靠性的潜力。然而，本研究仅限于乳腺癌病理报告，且使用的是从与单一机构相关的医院获取的数据。未来，我们计划逐步扩大本研究的范围，纳入其他癌症类型的病理报告，并进行外部验证，以进一步证实所提出系统的稳健性和通用性。通过这种技术整合，我们旨在证实生成式人工智能在提高数据处理速度和可靠性方面的能力。本研究结果证实，生成式人工智能能够显著改变病理报告的处理方式，有望通过促进对复杂医学数据的结构化分析，在生物医学研究中取得重大进展。
