If increasing practitioners’ diagnostic accuracy, medical artificial intelligence (AI) may lead to better treatment decisions at lower costs, while uncertainty remains around the resulting cost-effectiveness. In the present study, we assessed how enlarging the data set used for training an AI for caries detection on bitewings affects cost-effectiveness and also determined the value of information by reducing the uncertainty around other input parameters (namely, the costs of AI and the population’s caries risk profile). We employed a convolutional neural network and trained it on 10%, 25%, 50%, or 100% of a labeled data set containing 29,011 teeth without and 19,760 teeth with caries lesions stemming from bitewing radiographs. We employed an established health economic modeling and analytical framework to quantify cost-effectiveness and value of information. We adopted a mixed public–private payer perspective in German health care; the health outcome was tooth retention years. A Markov model, allowing to follow posterior teeth over the lifetime of an initially 12-y-old individual, and Monte Carlo microsimulations were employed. With an increasing amount of data used to train the AI sensitivity and specificity increased nonlinearly, increasing the data set from 10% to 25% had the largest impact on accuracy and, consequently, cost-effectiveness. In the base-case scenario, AI was more effective (tooth retention for a mean [2.5%–97.5%] 62.8 [59.2–65.5] y) and less costly (378 [284–499] euros) than dentists without AI (60.4 [55.8–64.4] y; 419 [270–593] euros), with considerable uncertainty. The economic value of reducing the uncertainty around AI’s accuracy or costs was limited, while information on the population’s risk profile was more relevant. When developing dental AI, informed choices about the data set size may be recommended, and research toward individualized application of AI for caries detection seems warranted to optimize cost-effectiveness.

如果提高从业者的诊断准确性，医疗人工智能 (AI) 可能会以更低的成本导致更好的治疗决策，而由此产生的成本效益仍然存在不确定性。在本研究中，我们评估了扩大用于训练 AI 以检测咬翼龋齿的数据集如何影响成本效益，并通过减少其他输入参数的不确定性（即 AI 的成本和人群的龋齿风险状况）。我们使用了一个卷积神经网络，并在 10%、25%、50% 或 100% 的标记数据集上对其进行了训练，该数据集包含来自咬翼 X 光片的 29,011 颗无龋齿和 19,760 颗有龋齿病变的牙齿。我们采用了既定的健康经济模型和分析框架来量化信息的成本效益和价值。我们在德国医疗保健中采用了混合的公私支付者视角；健康结果是牙齿保留年数。马尔可夫模型，允许在最初 12 岁的个体的一生中跟踪后牙，并采用蒙特卡罗微观模拟。随着用于训练 AI 敏感性和特异性的数据量的增加非线性增加，将数据集从 10% 增加到 25% 对准确性的影响最大，因此对成本效益的影响最大。在基本情况下，与没有 AI 的牙医（60.4 [55.8–64.4] 年；419 [270–593] 欧元），具有相当大的不确定性。减少 AI 准确性或成本不确定性的经济价值是有限的，而有关人群风险状况的信息则更为相关。在开发牙科 AI 时，可能会建议对数据集大小进行明智的选择，并且似乎有必要对 AI 用于龋齿检测的个性化应用进行研究，以优化成本效益。