Semerci Zeliha Merve, Yardımcı Selmi
Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Akdeniz University, Antalya 07070, Turkey.
Diagnostics (Basel). 2024 Jun 14;14(12):1260. doi: 10.3390/diagnostics14121260.
Advancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.
人工智能(AI)的进步有望在包括牙髓病学、口腔放射学、正畸学、儿童牙科、牙周病学、口腔修复学和牙体修复学在内的各种牙科领域引发变革性转变。这篇叙述性综述阐述了AI在提高诊断准确性、简化治疗计划以及可能揭示创新治疗方式方面日益重要的作用,从而提升患者护理标准。最近的分析证实了AI辅助方法相对于传统技术的优越性,肯定了它们在牙科护理中实现个性化、准确性和效率的能力。这些AI应用的核心是卷积神经网络和深度学习模型,它们在诊断、预后和治疗决策方面已显示出有效性,在某些复杂病例中,其表现超过了传统方法。尽管取得了这些进展,但将AI整合到临床实践中仍面临挑战,如数据安全问题、对AI生成结果透明度的要求,以及持续验证以确定AI工具的可靠性和适用性的必要性。本综述强调了AI在牙科实践中的潜在益处,将AI视为强化牙科专业的辅助工具,而非牙科专业人员的替代品。虽然AI预示着诊断、治疗计划和个性化护理方面的改善,但必须谨慎应对伦理和实际考量,以确保牙科领域AI的负责任发展。