Shujaat Sohaib
King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, P.O. Box 3660, Riyadh 11481, Saudi Arabia.
OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium.
Diagnostics (Basel). 2025 Jan 24;15(3):273. doi: 10.3390/diagnostics15030273.
The adoption of automated machine learning (AutoML) in dentistry is transforming clinical practices by enabling clinicians to harness machine learning (ML) models without requiring extensive technical expertise. This narrative review aims to explore the impact of autoML in dental applications. A comprehensive search of PubMed, Scopus, and Google Scholar was conducted without time and language restrictions. Inclusion criteria focused on studies evaluating autoML applications and performance for dental tasks. Exclusion criteria included non-dental studies, single-case reports, and conference abstracts. This review highlights multiple promising applications of autoML in dentistry. Diagnostic tasks showed high accuracy, such as 95.4% precision in dental implant classification and 92% accuracy in paranasal sinus disease detection. Predictive tasks also demonstrated promise, including 84% accuracy for ICU admissions due to dental infections and 93.9% accuracy in orthodontic extraction predictions. AutoML frameworks like Google Vertex AI and H2O AutoML emerged as key tools for these applications. AutoML shows great promise in transforming dentistry by facilitating data-driven decision-making and improving patient care quality through accessible, automated solutions. Future advancements should focus on enhancing model interpretability, developing large and annotated datasets, and creating pipelines tailored to dental tasks. Educating clinicians on autoML and integrating domain-specific knowledge into automated platforms could further bridge the gap between complex ML technology and practical dental applications.
在牙科领域采用自动化机器学习（AutoML）正在改变临床实践，使临床医生无需广泛的技术专长就能利用机器学习（ML）模型。本叙述性综述旨在探讨AutoML在牙科应用中的影响。对PubMed、Scopus和谷歌学术进行了全面搜索，没有时间和语言限制。纳入标准侧重于评估AutoML在牙科任务中的应用和性能的研究。排除标准包括非牙科研究、单病例报告和会议摘要。本综述强调了AutoML在牙科领域的多个有前景的应用。诊断任务显示出高准确性，例如牙种植体分类的精度为95.4%，鼻窦疾病检测的准确率为92%。预测任务也显示出前景，包括因牙科感染入住重症监护病房的预测准确率为84%，正畸拔牙预测的准确率为93.9%。像谷歌Vertex AI和H2O AutoML这样的AutoML框架成为这些应用的关键工具。AutoML通过促进数据驱动的决策制定，并通过可访问的自动化解决方案提高患者护理质量，在改变牙科领域方面显示出巨大的前景。未来的进展应侧重于提高模型的可解释性、开发大型带注释的数据集，以及创建针对牙科任务的管道。对临床医生进行AutoML教育，并将特定领域的知识整合到自动化平台中，可能会进一步弥合复杂的ML技术与实际牙科应用之间的差距。
