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TRUVACE RECORD VERSION record: TRV-2026-0277 version: 1 kind: certified reason: Certified into the record timestamp: 2026-07-19T01:15:19.637240Z status: published lens: g_space sector: health headline: Digital Twins’ Advancements and Applications in Healthcare, Towards Precision Medicine dek: 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 inf… gain_title: AI-generated digital twins of patients enable comprehensive predictions of future health outcomes and optimization of individualized treatment plans. problem_title: (none) trace_subject: (none) gain_reading: AI-generated digital twins of patients enable comprehensive predictions of future health outcomes and optimization of individualized treatment plans. gain_evidence: enhance the customization and optimization of treatment plans | AI models can now generate comprehensive predictions of future health outcomes for specific patients problem_reading: (none) problem_evidence: (none) quick_read: Published November 11 2024, this review examines Digital Twins and Digital Human Twins as virtual replicas of patients that combine physiological data with AI models to predict health outcomes and tailor therapies. It matters because it points to a shift toward precision medicine via real-time digital replicas, but its clinical value remains uncertain until challenges of security, bias, accessibility and data quality are resolved. limitation: Integration is constrained by unresolved issues of data security, accessibility, bias, and quality that must be addressed before clinical deployment. tag: Evidence-backed gain key_points: Digital Twins are virtual replicas encapsulating medical and physiological characteristics such as tissues, organs, and biokinetic data. | Dynamic bidirectional connections enable real-time data exchange transforming electronic health records. | AI models leverage historical datasets from clinical trials and real-world sources to generate patient-specific predictions. rundown: The review describes DTs and Digital Human Twins as virtual replicas of tissues, organs and biokinetic data with dynamic bidirectional links to physical counterparts for real-time exchange. It notes AI models trained on extensive historical clinical trial and real-world datasets can produce AI-generated DTs that predict diagnoses, disease progression and treatment responses. sources: - peer_reviewed | Journal of Personalized Medicine | https://doi.org/10.3390/jpm14111101 | 2024-11-11 prev: 0000000000000000000000000000000000000000000000000000000000000000
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