人工智能（AI）可以通过广泛的学习获得人类尚不知道的特征，从而能够处理大量的病理图像数据。人工智能分为机器学习和深度学习，具有处理大量数据和处理图像分析的优势，因此在准确评估肿瘤微环境（TME）模型方面也具有巨大潜力。由于TME成分复杂，对TME的深入研究有助于为治疗、评估患者术后治疗反应和预后预测提供新思路。本研究回顾了人工智能在TME评估中的应用进展，概述了人工智能技术在医学中的应用，深入探讨了人工智能在分析各种细胞（肿瘤细胞、免疫细胞）的数量和空间位置特征中的应用。和非免疫细胞）在TME中的应用，揭示了TME的预测预后价值，为肿瘤治疗提供了新思路，凸显了临床应用的巨大潜力。此外，还讨论了其局限性并鼓励其实际临床应用的未来方向。© 2024。作者。

Artificial intelligence (AI) can acquire characteristics that are not yet known to humans through extensive learning, enabling to handle large amounts of pathology image data. Divided into machine learning and deep learning, AI has the advantage of handling large amounts of data and processing image analysis, consequently it also has a great potential in accurately assessing tumour microenvironment (TME) models. With the complex composition of the TME, in-depth study of TME contributes to new ideas for treatment, assessment of patient response to postoperative therapy and prognostic prediction. This leads to a review of the development of AI's application in TME assessment in this study, provides an overview of AI techniques applied to medicine, delves into the application of AI in analysing the quantitative and spatial location characteristics of various cells (tumour cells, immune and non-immune cells) in the TME, reveals the predictive prognostic value of TME and provides new ideas for tumour therapy, highlights the great potential for clinical applications. In addition, a discussion of its limitations and encouraging future directions for its practical clinical application is presented.© 2024. The Author(s).