计算病理学（CPath）算法能够检测、分割或分类癌症全切片图像，在准确性方面接近甚至超过病理学家。在这些算法能够在实践中使用之前，必须克服一些挑战。因此，我们的目标是通过关注对于影响因素的看法和初步经验，探索国际上关于CPath在肿瘤病理学中未来角色的观点。我们采用了国际探索性电子调查和半结构化面试的方法，使用一个实施框架来分类潜在的影响因素。电子调查结果显示在态度、可理解性和CPath的验证方面，观点存在显著的变化。面试结果显示，障碍集中在可用证据的质量，而大部分促进因素涉及CPath的优点。在多个因素中存在共识不足，如确定使用CPath进行足够验证的方法，CPath在数字工作流中的首选功能以及CPath在病理学教育中引入的时间。观点多样性表明CPath采用中存在多种影响因素。下一步将是定量确定采用的重要因素并启动验证研究。这两者都应包括清晰的病例描述，并在基于亚专业的更同质化的病理学家小组中进行。© 2023年作者(s)。

Computational pathology (CPath) algorithms detect, segment or classify cancer in whole slide images, approaching or even exceeding the accuracy of pathologists. Challenges have to be overcome before these algorithms can be used in practice. We therefore aim to explore international perspectives on the future role of CPath in oncological pathology by focusing on opinions and first experiences regarding barriers and facilitators. We conducted an international explorative eSurvey and semi-structured interviews with pathologists utilizing an implementation framework to classify potential influencing factors. The eSurvey results showed remarkable variation in opinions regarding attitude, understandability and validation of CPath. Interview results showed that barriers focused on the quality of available evidence, while most facilitators concerned strengths of CPath. A lack of consensus was present for multiple factors, such as the determination of sufficient validation using CPath, the preferred function of CPath within the digital workflow and the timing of CPath introduction in pathology education. The diversity in opinions illustrates variety in influencing factors in CPath adoption. A next step would be to quantitatively determine important factors for adoption and initiate validation studies. Both should include clear case descriptions and be conducted among a more homogenous panel of pathologists based on sub specialization.© 2023. The Author(s).