预算影响模型 (BIM) 可以预测采用新技术的财务影响以及预算重新分配的潜在需求，从而在报销决策中发挥至关重要的作用。尽管准确预测很重要，但研究表明估计与现实之间存在巨大差异。我们正在开发一种基于人工智能的临床决策工具，以识别最有可能从免疫治疗中受益的非小细胞肺癌患者。为了评估预算影响并描述已发表的肺癌 BIM 的系统文献综述。我们搜索了 PubMed EMBASE 用于 2010 年至 2023 年间发表的研究，其中包括描述肺癌干预措施的 BIM。对所有合格研究进行前向和后向参考检索。我们提取了作者和出版年份、国家、干预措施、疾病阶段、时间范围、分析视角、使用的建模方法、包括的成本类型、进行的敏感性分析以及使用的数据源。然后，我们评估了对健康经济学和药物经济学研究专业协会最佳实践指南的遵守情况。共确定了 25 个 BIM，涵盖 14 个不同的国家。近一半的模型无法明确确定模型结构。成本计算器方法是其中最常见的方法。根据建议，时间范围为 1 至 5 年。大多数模型比较药物，4 个比较非药物干预，7 个比较诊断技术。关于市场接受度的假设缺乏记录且缺乏动机。很少将癌症相关费用纳入其中。对最佳实践的遵守情况各不相同，并且似乎没有随着时间的推移而改善。已发布的肺癌 BIM 数量超出了预期。随着时间的推移，发表频率和模型质量呈现出适度的趋势。我们的分析揭示了模型之间的可变性以及它们对最佳实践的遵守，表明还有很大的改进空间。尽管没有一个模型单独适合评估基于人工智能的治疗选择工具，但一些模型提供了有价值的见解。

Budget impact models (BIMs) forecast the financial implications of adopting new technologies and the potential need for budget reallocation, thus playing a crucial role in reimbursement decisions. Despite the importance of accurate forecasts, studies indicate large discrepancies between estimates and reality. We are developing an artificial intelligence-based clinical decision tool to identify patients with non-small cell lung cancer who are most likely to benefit from immunotherapy.To evaluate the budgetary implications and describe a systematic literature review of published lung cancer BIMs.We searched PubMed and EMBASE for studies published between 2010 and 2023 that include BIMs that describe lung cancer interventions. Forward and backward reference searches were performed for all qualifying studies. We extracted author and publication year, country, interventions, disease stages, time horizon, analytical perspective, modeling methods used, types of costs included, sensitivity analyses conducted, and data sources used. We then evaluated adherence to the Professional Society for Health Economics and Pharmacoeconomics Research best-practice guidelines.A total of 25 BIMs were identified, spanning 14 different countries. Model structure could not be ascertained definitively for nearly half of the models. The cost calculator approach was most common among the others. Time horizons ranged from 1 to 5 years, in line with recommendations. Most models compared drugs, 4 compared nondrug interventions, and 7 compared diagnostic technologies. Assumptions about market uptake were poorly documented and poorly motivated. Inclusion of cancer-related costs was rare. Adherence to best practices was variable and did not appear to improve over time.The number of published BIMs for lung cancer exceeded expectations. There were modest trends toward publication frequency and model quality over time. Our analysis revealed variability across the models, as well as their adherence to best practices, indicating substantial room for improvement. Although none of the models were individually suitable for the purpose of evaluating an artificial intelligence-based treatment selection tool, some models provided valuable insights.