Jariyapan P, Pora W, Kasamsumran N, Lekawanvijit S
Bangkok International Preparatory and Secondary School, Bangkok, Thailand.
Chulalongkorn University, Faculty of Engineering, Department of Electrical Engineering, Bangkok, Thailand.
Malays J Pathol. 2025 Apr;47(1):3-12.
Currently, digital pathology is a profound transformation in the field of pathology. Numerous artificial intelligence (AI) algorithms have demonstrated significant potential for the improvement of diagnostic efficiency, morphometric analysis of biomarkers, and diagnostic screening. However, the application of AI in pathology is a matter of considerable worry among pathologists. Within this article, we provided a concise overview of the process of digital pathology and deep learning in diagnostic pathology. Additionally, we explored the advantages and uses, obstacles and constraints, and future potential of artificial intelligence in diagnostic pathology. The implementation of innovative AI-based methods in pathology laboratory processes will enhance the effectiveness of disease diagnosis, as the collaboration between pathologists and AI systems has demonstrated superior performance compared to both the individual pathologist and the system. Nevertheless, pathologists continue to be crucial in the finalisation of the diagnosis.
目前，数字病理学是病理学领域的一项深刻变革。众多人工智能（AI）算法已在提高诊断效率、生物标志物形态计量分析和诊断筛查方面展现出巨大潜力。然而，AI在病理学中的应用是病理学家相当担忧的问题。在本文中，我们简要概述了数字病理学和深度学习在诊断病理学中的过程。此外，我们探讨了人工智能在诊断病理学中的优势与用途、障碍与限制以及未来潜力。在病理实验室流程中实施基于AI的创新方法将提高疾病诊断的有效性，因为病理学家与AI系统之间的协作已显示出比个体病理学家和系统单独工作更优越的性能。尽管如此，病理学家在最终诊断中仍然至关重要。
