Papachristou Konstantinos, Katsakiori Paraskevi F, Papadimitroulas Panagiotis, Strigari Lidia, Kagadis George C
3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece.
Bioemission Technology Solutions, BIOEMTECH, 15344 Athens, Greece.
J Pers Med. 2024 Nov 11;14(11):1101. doi: 10.3390/jpm14111101.
This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics-such as tissues, organs, and biokinetic data-of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental influences. By establishing dynamic, bidirectional connections between the DTs of physical objects and their digital counterparts, these technologies enable real-time data exchange, thereby transforming electronic health records. Leveraging the increasing availability of extensive historical datasets from clinical trials and real-world sources, AI models can now generate comprehensive predictions of future health outcomes for specific patients in the form of AI-generated DTs. Such models can also offer insights into potential diagnoses, disease progression, and treatment responses. This remarkable progression in healthcare paves the way for precision medicine and personalized health, allowing for high-level individualized medical interventions and therapies. However, the integration of DTs into healthcare faces several challenges, including data security, accessibility, bias, and quality. Addressing these obstacles is crucial to realizing the full potential of DHTs, heralding a new era of personalized, precise, and accurate medicine.
本综述探讨了数字孪生（DTs）及其变体数字人类孪生（DHTs）对医疗保健领域的重大影响。数字孪生代表虚拟复制品，它封装了患者的医学和生理特征，如组织、器官和生物动力学数据。这些虚拟模型通过对遗传因素和环境影响之间的复杂相互作用进行建模，有助于更深入地了解疾病进展，并增强治疗方案的定制和优化。通过在物理对象的数字孪生与其数字对应物之间建立动态、双向连接，这些技术实现了实时数据交换，从而改变了电子健康记录。利用来自临床试验和现实世界来源的大量历史数据集的可用性不断提高，人工智能模型现在可以以人工智能生成的数字孪生的形式，为特定患者生成未来健康结果的全面预测。此类模型还可以提供有关潜在诊断、疾病进展和治疗反应的见解。医疗保健领域的这一显著进展为精准医学和个性化健康铺平了道路，允许进行高级别的个性化医疗干预和治疗。然而，将数字孪生整合到医疗保健中面临着几个挑战，包括数据安全、可访问性、偏差和质量。解决这些障碍对于实现数字人类孪生的全部潜力至关重要，这预示着个性化、精确和准确医学的新时代。
