Background: In the face of rapid technological advances in computational cytology including artificial intelligence (AI), optimization of its application to clinical practice would benefit from reflection on the lessons learned from the decades-long journey in the development of computer-assisted Pap test screening. Summary: The initial driving force for automated screening in cytology was the overwhelming number of Pap tests requiring manual screening, leading to workflow backlogs and incorrect diagnoses. Several companies invested resources to address these concerns utilizing different specimen processing techniques and imaging systems. However, not all companies were commercially prosperous. Successful implementation of this new technology required viable use cases, improved clinical outcomes, and an acceptable means of integration into the daily workflow of cytopathology laboratories. Several factors including supply and demand, Food and Drug Administration (FDA) oversight, reimbursement, overcoming learning curves and workflow changes associated with the adoption of new technology, and cytologist apprehension, played a significant role in either promoting or preventing the widespread adoption of automated screening technologies. Key Messages: Any change in health care, particularly those involving new technology that impacts clinical workflow, is bound to have its successes and failures. However, perseverance through learning curves, optimizing workflow processes, improvements in diagnostic accuracy, and regulatory and financial approval can facilitate widespread adoption of these technologies. Given their history with successfully implementing automated Pap test screening, cytologists are uniquely positioned to not only help with the development of AI technology for other areas of pathology, but also to guide how they are utilized, regulated, and managed.
Acta Cytologica

中文翻译：

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计算细胞学：从巴氏试验计算机辅助筛查中吸取的教训。

背景：面对包括人工智能 (AI) 在内的计算细胞学技术的快速进步，对其在临床实践中的应用的优化将受益于对计算机辅助巴氏试验筛查数十年发展历程中的经验教训的反思. 概括：细胞学自动化筛查的最初驱动力是大量需要手动筛查的巴氏试验，导致工作流程积压和错误诊断。几家公司投入资源，利用不同的标本处理技术和成像系统来解决这些问题。然而，并非所有公司都在商业上繁荣。这项新技术的成功实施需要可行的用例、改善的临床结果以及可接受的整合到细胞病理学实验室日常工作流程中的方法。几个因素包括供需、食品和药物管理局 (FDA) 监督、报销、克服与采用新技术相关的学习曲线和工作流程变化以及细胞学家的担忧，关键信息：医疗保健方面的任何变化，尤其是那些涉及影响临床工作流程的新技术的变化，都必然有其成功和失败。然而，坚持学习曲线、优化工作流程、提高诊断准确性以及监管和财务批准可以促进这些技术的广泛采用。鉴于他们成功实施自动化巴氏试验筛查的历史，细胞学家具有独特的优势，不仅可以帮助开发用于其他病理学领域的 AI 技术，还可以指导如何利用、监管和管理这些技术。
细胞学学报