Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight.

翻译：人工智能代表着人类医学的新前沿，它能够挽救更多生命、降低成本，从而提高医疗可及性。因此，人工智能在癌症医学成像，尤其是组织病理学领域的发展速度迅猛，这也引发了可能阻碍其融入现有系统的伦理与技术问题。为了描绘人工智能在癌症组织成像中的应用路径，我们回顾了当前的研究工作，并明确了人工智能如何改善癌症病理诊断与研究。在此综述中，我们归纳了模型开发的5项核心任务，包括回归、分类、分割、生成和压缩任务。我们探讨了这些方法所面临的益处与挑战，以及如何将其调整用于癌症的预防与治疗。本文所研究的文献代表了这一领域的开端，未来的实验将建立在我们所强调的基础之上。