在自身免疫性疾病的诊断、病情监测、预后评估中，自身抗体等相关实验室检测指标具有重要价值，但传统检测实践面临策略不合理、阳性预测值偏低、医疗资源浪费等问题。循证医学强调基于最佳研究证据指导临床决策，机器学习凭借其强大的数据挖掘能力和分析能力，为临床检验项目的管理与选择提供了强有力的支持。本文探讨了基于循证医学与机器学习的自身免疫性疾病检测项目新策略在疾病诊断、分层管理、预后评估中的价值，以系统性红斑狼疮为例构建自身免疫性疾病检测项目组合新策略。新策略在提升诊断效能、强化动态管理及精准分配医疗资源方面展现出显著潜力。但其临床推广仍面临检测平台标准化、训练数据质量及医疗信息化融合等多重挑战。未来，通过多中心数据的积累与临床决策支持系统的深度融合，有望构建质量、效率与成本最优的自身免疫性疾病检测项目组合新策略。

In the diagnosis, disease monitoring, and prognosis evaluation of autoimmune diseases, laboratory markers such as autoantibodies hold significant value. However, traditional testing practices face challenges including suboptimal strategies, low positive predictive value, and wastage of medical resources. Evidence-based medicine emphasizes guiding clinical decisions based on the best available research evidence, while machine learning, with its powerful data mining and analytical capabilities, provides robust support for the management and selection of clinical laboratory tests. This article explores the value of a novel strategy for autoimmune disease testing project selection, grounded in evidence-based medicine and machine learning, in disease diagnosis, stratified management, and prognosis evaluation. Using systemic lupus erythematosus as an example, a novel test pannel strategy for autoimmune disease testing panels is constructed. This novel strategy demonstrates significant potential in enhancing diagnostic efficacy, strengthening dynamic management, and optimizing the allocation of medical resources. However, its clinical implementation still faces multiple challenges, including standardization of testing platforms, quality of training data, and integration with healthcare information systems. In the future, through the accumulation of multi-center data and deep integration with clinical decision support systems, it is expected to establish a novel strategy for autoimmune disease testing panel selection that optimizes quality, efficiency, and cost.