机器学习 (ML) 已展现出增强成人肿瘤学护理的潜力。然而，其在儿科血液恶性肿瘤中的应用仍在不断涌现，有必要对其在该领域的能力和局限性进行全面审查。通过Ovid进行了文献检索。研究重点是患有血液系统恶性肿瘤的儿科患者的机器学习模型。研究被分为主题组进行分析。本综述纳入了 20 项研究，主要针对白血病。研究按主题类别进行组织，例如预后、治疗反应和毒性预测。预后研究显示 AUC 评分在 0.685 至 0.929 之间，表明预测准确性为中高。治疗反应研究表明 AUC 评分在 0.840 至 0.875 之间，反映了中等准确性。毒性预测研究报告了较高的准确性，AUC 评分为 0.870 至 0.927。只有五项研究 (25%) 进行了外部验证。研究中的机器学习任务、报告格式和效果测量存在显着的异质性，凸显了标准化报告的缺乏和数据可比性方面的挑战。这些机器学习模型的临床适用性仍然因缺乏外部验证和方法异质性而受到限制。需要通过标准化报告和严格的外部验证来应对这些挑战，将机器学习从一种有前途的研究工具转化为可靠的临床实践组成部分。关键信息：机器学习 (ML) 显着增强了儿科血液癌症的预测模型，为个性化治疗提供了新途径策略。未来的研究应该集中于开发可以与实时临床工作流程集成的机器学习模型。文献补充：提供当前 ML 应用和趋势的全面概述。它指出了其适用性的局限性，包括数据集的多样性有限，这可能会影响 ML 模型在不同人群中的通用性。鼓励 ML 研究的标准化和外部验证，旨在通过儿科血液肿瘤学的精准医疗来改善患者的治疗结果。© 2024 . 作者。

Machine Learning (ML) has demonstrated potential in enhancing care in adult oncology. However, its application in paediatric haematological malignancies is still emerging, necessitating a comprehensive review of its capabilities and limitations in this area.A literature search was conducted through Ovid. Studies included focused on ML models in paediatric patients with haematological malignancies. Studies were categorised into thematic groups for analysis.Twenty studies, primarily on leukaemia, were included in this review. Studies were organised into thematic categories such as prognoses, treatment responses and toxicity predictions. Prognostic studies showed AUC scores between 0.685 and 0.929, indicating moderate-high predictive accuracy. Treatment response studies demonstrated AUC scores between 0.840 and 0.875, reflecting moderate accuracy. Toxicity prediction studies reported high accuracy with AUC scores from 0.870 to 0.927. Only five studies (25%) performed external validation. Significant heterogeneity was noted in ML tasks, reporting formats, and effect measures across studies, highlighting a lack of standardised reporting and challenges in data comparability.The clinical applicability of these ML models remains limited by the lack of external validation and methodological heterogeneity. Addressing these challenges through standardised reporting and rigorous external validation is needed to translate ML from a promising research tool into a reliable clinical practice component.Key message: Machine Learning (ML) significantly enhances predictive models in paediatric haematological cancers, offering new avenues for personalised treatment strategies. Future research should focus on developing ML models that can integrate with real-time clinical workflows. Addition to literature: Provides a comprehensive overview of current ML applications and trends. It identifies limitations to its applicability, including the limited diversity in datasets, which may affect the generalisability of ML models across different populations.Encourages standardisation and external validation in ML studies, aiming to improve patient outcomes through precision medicine in paediatric haematological oncology.© 2024. The Author(s).