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Artificial Intelligence in Dentistry: Chances and Challenges

机译：牙科领域的人工智能：机遇与挑战

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The term “artificial intelligence” (AI) refers to the idea of machines being capable of performing human tasks. A subdomain of AI is machine learning (ML), which “learns” intrinsic statistical patterns in data to eventually cast predictions on unseen data. Deep learning is a ML technique using multi-layer mathematical operations for learning and inferring on complex data like imagery. This succinct narrative review describes the application, limitations and possible future of AI-based dental diagnostics, treatment planning, and conduct, for example, image analysis, prediction making, record keeping, as well as dental research and discovery. AI-based applications will streamline care, relieving the dental workforce from laborious routine tasks, increasing health at lower costs for a broader population, and eventually facilitate personalized, predictive, preventive, and participatory dentistry. However, AI solutions have not by large entered routine dental practice, mainly due to 1) limited data availability, accessibility, structure, and comprehensiveness, 2) lacking methodological rigor and standards in their development, 3) and practical questions around the value and usefulness of these solutions, but also ethics and responsibility. Any AI application in dentistry should demonstrate tangible value by, for example, improving access to and quality of care, increasing efficiency and safety of services, empowering and enabling patients, supporting medical research, or increasing sustainability. Individual privacy, rights, and autonomy need to be put front and center; a shift from centralized to distributed/federated learning may address this while improving scalability and robustness. Lastly, trustworthiness into, and generalizability of, dental AI solutions need to be guaranteed; the implementation of continuous human oversight and standards grounded in evidence-based dentistry should be expected. Methods to visualize, interpret, and explain the logic behind AI solutions will contribute (“explainable AI”). Dental education will need to accompany the introduction of clinical AI solutions by fostering digital literacy in the future dental workforce.
机译：术语“人工智能”（AI）是指机器能够执行人工任务的想法。 AI的一个子域是机器学习（ML），它可以“学习”数据中的固有统计模式，从而最终对看不见的数据进行预测。深度学习是一种ML技术，它使用多层数学运算来学习和推断图像等复杂数据。这份简短的叙述性综述描述了基于AI的牙科诊断，治疗计划和行为（例如图像分析，预测制作，记录保存以及牙科研究和发现）的应用，局限性和可能的​​未来。基于AI的应用程序将简化护理流程，从繁重的日常工作中解放出来，以较低的成本为更广泛的人群增加健康水平，并最终促进个性化，预测性，预防性和参与性牙科。但是，人工智能解决方案尚未进入常规的常规牙科实践中，主要是因为1）数据可用性，可访问性，结构和全面性有限，2）在开发过程中缺乏方法上的严谨性和标准，3）围绕价值和实用性的实际问题这些解决方案，还有道德和责任。牙科领域的任何AI应用都应通过例如改善护理的获取和护理质量，提高服务的效率和安全性，增强患者的能力和能力，支持医学研究或提高可持续性等方法来显示有形的价值。个人隐私，权利和自主权必须放在首位。从集中式学习到分布式/联合学习的转变可以解决此问题，同时提高可扩展性和健壮性。最后，需要确保牙科AI解决方案的可信度和推广性。应当期望实施持续的人类监督和基于循证牙科的标准。可视化，解释和解释AI解决方案背后的逻辑的方法将有所作为（“可解释的AI”）。牙科教育将需要通过在未来的牙科劳动力中培养数字素养来伴随临床AI解决方案的引入。

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