Yang H X, Xiong J, Zhao W L
Department of Haematology, State Key Laboratory of Medical Genomics, Shanghai Institute of Haematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Zhonghua Xue Ye Xue Za Zhi. 2025 Feb 14;46(2):186-192. doi: 10.3760/cma.j.cn121090-20241022-00409.
Hematological malignancy is a highly heterogeneous disease with complex biological characteristics and diverse clinical manifestations. Therefore, precise diagnosis and treatment are crucial and urgently needed. To further improve the accuracy of diagnosis and prognostication and to promote personalized therapy, artificial intelligence (AI) has been increasingly used. This study reviewed literature published in the last 5 years and summarized the application, benefits, and drawbacks of AI in the diagnosis, treatment, and prognosis of hematologic malignancies. Although AI can effectively improve the accuracy of diagnosis and therapy, low-quality data, poor interpretability of the model, and limited clinical transformation have impeded its popularization and application. In the future, the clinical application of AI in hematologic malignancy can be accelerated by establishing standards for clinical data processing, integrating multimodal information for accurate diagnosis and prognostication, and conducting systematic clinical verification of model algorithms.
血液系统恶性肿瘤是一种具有高度异质性的疾病,其生物学特性复杂,临床表现多样。因此,精确的诊断和治疗至关重要且迫切需要。为了进一步提高诊断和预后评估的准确性,并促进个性化治疗,人工智能(AI)已得到越来越多的应用。本研究回顾了过去5年发表的文献,总结了人工智能在血液系统恶性肿瘤的诊断、治疗和预后评估中的应用、优势和不足。尽管人工智能可以有效提高诊断和治疗的准确性,但数据质量低、模型可解释性差以及临床转化受限阻碍了其推广应用。未来,通过建立临床数据处理标准、整合多模态信息以进行准确诊断和预后评估,以及对模型算法进行系统的临床验证,可以加速人工智能在血液系统恶性肿瘤中的临床应用。