Artificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery. Nine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2. Authors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%. While AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows. 2A.

中文翻译：

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小儿外科人工智能的现状：系统回顾

人工智能 (AI) 最近被证明可以改善临床工作流程和结果 - 但其在儿科手术中的潜力在很大程度上仍未得到开发。这篇系统综述详细介绍了人工智能在儿科手术中的应用。从成立到 2023 年 1 月，我们对 9 个医学数据库进行了搜索，找到了关注儿科手术中人工智能的文章。两位作者审阅了符合条件的文章的全文。如果研究是针对主要通过手术治疗的儿科健康状况的人工智能模型的开发、验证或临床应用的原始调查，则纳入研究。如果研究未经同行评审，属于评论文章、社论、评论或病例报告，不关注儿科手术条件，或未采用至少一种人工智能模型，则被排除在外。使用 PROBAST 和 QUADAS-2 提取的数据包括研究特征、临床专业、AI 方法和算法类型、AI 模型（算法）角色和性能指标、关键结果、可解释性、验证和偏倚风险。作者筛选了 8178 篇文章，纳入了 112 篇。一半的研究 (50%) 报告了预测模型（不良事件 [25%]、手术结果 [16%] 和生存 [9%]），其次是诊断 (29%) 和决策支持模型（21%）。神经网络（44%）和集成学习器（36%）是跨应用领域最常用的人工智能方法。所有模型中主要的儿科外科亚专科是普通外科（31%）和神经外科（25%）。 44% 的模型是可解释的，6% 的模型既可解释又经过外部验证。 40% 的模型存在很高的偏差风险，7% 的模型对适用性存在担忧。虽然人工智能在儿科手术中具有广泛的潜在临床应用，但很少有已发表的人工智能算法经过外部验证、可解释和公正。未来的研究需要重点开发人工智能模型，这些模型经过前瞻性验证并最终集成到临床工作流程中。 2A。