Department of Epidemiology and Public Health, Foch Hospital, 92150, Suresnes, France.
Eur Arch Otorhinolaryngol. 2024 Nov;281(11):6155-6159. doi: 10.1007/s00405-024-08662-5. Epub 2024 May 4.
Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes.
These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems.
Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues.
Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research.
These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.
数字孪生技术在耳鼻喉科（ORL）领域开创了一个变革性的时代，将物理世界和数字世界融合在一起，提供物理实体或过程的动态、虚拟模型。
这些模型能够模拟、预测和优化真实世界的对应物，正在从静态复制品发展为智能、自适应系统。
得益于通信、传感器技术、大数据分析、物联网（IoT）和仿真技术的进步，人工智能（AI）在耳鼻喉科的数字孪生技术有望实现个性化治疗计划、虚拟实验和治疗干预优化。尽管具有潜力，但数字孪生在耳鼻喉科的整合面临着一些挑战，包括数据隐私和安全、数据集成和互操作性、计算需求、模型验证和准确性、伦理和监管考虑、患者参与以及成本和可及性问题。
克服这些挑战需要强大的数据保护措施、无缝的数据集成、大量的计算资源、严格的验证研究、伦理透明度、患者教育以及使技术可及和负担得起。展望未来，耳鼻喉科数字孪生的未来前景光明，人工智能和机器学习的进步、组学数据集成、实时监测、虚拟临床试验、患者赋权、无缝医疗保健整合、纵向数据分析和合作研究都将推动这一领域的发展。
这些发展有望完善诊断和治疗策略、提升患者护理水平，并促进更高效和定制化的耳鼻喉科研究，最终实现更有效和个性化的耳鼻喉科管理。
