Artificial intelligence and machine learning (AI/ML) could enhance the ability to detect patterns of clinical characteristics in low-back pain (LBP) and guide treatment. We conducted three systematic reviews to address the following aims: (a) review the status of AI/ML research in LBP, (b) compare its status to that of two established LBP classification systems (STarT Back, McKenzie). AI/ML in LBP is in its infancy: 45 of 48 studies assessed sample sizes <1000 people, 19 of 48 studies used ≤5 parameters in models, 13 of 48 studies applied multiple models and attained high accuracy, 25 of 48 studies assessed the binary classification of LBP versus no-LBP only. Beyond the 48 studies using AI/ML for LBP classification, no studies examined use of AI/ML in prognosis prediction of specific sub-groups, and AI/ML techniques are yet to be implemented in guiding LBP treatment. In contrast, the STarT Back tool has been assessed for internal consistency, test−retest reliability, validity, pain and disability prognosis, and influence on pain and disability treatment outcomes. McKenzie has been assessed for inter- and intra-tester reliability, prognosis, and impact on pain and disability outcomes relative to other treatments. For AI/ML methods to contribute to the refinement of LBP (sub-)classification and guide treatment allocation, large data sets containing known and exploratory clinical features should be examined. There is also a need to establish reliability, validity, and prognostic capacity of AI/ML techniques in LBP as well as its ability to inform treatment allocation for improved patient outcomes and/or reduced healthcare costs.

人工智能和机器学习 (AI/ML) 可以增强检测腰痛 (LBP) 临床特征模式并指导治疗的能力。我们进行了三项系统回顾，以实现以下目标：（a）回顾 LBP 中 AI/ML 研究的现状，（b）将其现状与两个已建立的 LBP 分类系统（STarT Back，McKenzie）进行比较。LBP 中的 AI/ML 尚处于起步阶段：48 项研究中的 45 项评估的样本量小于 1000 人，48 项研究中的 19 项在模型中使用≤5 个参数，48 项研究中的 13 项应用了多个模型并获得了高精度，48 项研究中的 25 项评估了LBP 与仅无 LBP 的二元分类。除了 48 项使用 AI/ML 进行 LBP 分类的研究之外，没有研究检验 AI/ML 在特定亚组预后预测中的应用，并且 AI/ML 技术尚未应用于指导 LBP 治疗。相比之下，STarT Back 工具已针对内部一致性、重测可靠性、有效性、疼痛和残疾预后以及对疼痛和残疾治疗结果的影响进行了评估。与其他治疗相比，麦肯齐的测试者间和内部可靠性、预后以及对疼痛和残疾结果的影响进行了评估。为了使 AI/ML 方法有助于细化 LBP（子）分类并指导治疗分配，应检查包含已知和探索性临床特征的大数据集。还需要确定 LBP 中 AI/ML 技术的可靠性、有效性和预后能力，以及其为治疗分配提供信息以改善患者结果和/或降低医疗成本的能力。