Digital twins for health: a scoping review

npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)

Published: March 22, 2024

Abstract The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. concept twin for health (DT4H) holds great promise to revolutionize entire system, including management delivery, disease treatment prevention, well-being maintenance, ultimately improving human life. rapid growth big data continuous advancement science (DS) artificial intelligence (AI) have potential significantly expedite DT research development by providing scientific expertise, essential data, robust cybertechnology infrastructure. Although initiatives been underway industry, government, military, DT4H is still its early stages. This paper presents an overview current applications DTs healthcare, examines consortium centers their limitations, surveys landscape emerging opportunities healthcare. We envision emergence collaborative global effort among stakeholders enhance improve quality life millions individuals worldwide through pioneering realm technology.

Language: Английский

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Digital twins in medicine

Nature Computational Science, Journal Year: 2024, Volume and Issue: 4(3), P. 184 - 191

Published: March 26, 2024

Language: Английский

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Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data

Annual Review of Biomedical Engineering, Journal Year: 2024, Volume and Issue: 26(1), P. 529 - 560

Published: April 10, 2024

Despite the remarkable advances in cancer diagnosis, treatment, and management over past decade, malignant tumors remain a major public health problem. Further progress combating may be enabled by personalizing delivery of therapies according to predicted response for each individual patient. The design personalized requires integration patient-specific information with an appropriate mathematical model tumor response. A fundamental barrier realizing this paradigm is current lack rigorous yet practical theory initiation, development, invasion, therapy. We begin review overview different approaches modeling growth including mechanistic as well data-driven models based on big data artificial intelligence. then present illustrative examples manifesting their utility discuss limitations stand-alone models. potential not only predicting but also optimizing therapy basis. describe efforts future possibilities integrate conclude proposing five challenges that must addressed fully realize care patients driven computational

Language: Английский

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Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: May 6, 2024

The impact of artificial intelligence (AI) will be felt not only in the arena patient care and deliverable therapies but also uniquely disruptive medical education healthcare simulation (HCS), particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability AI and, therefore, most practical place to test new applications. This ensure acquisition literacy graduates from country's various professional schools. Artificial has proven a useful adjunct developing interprofessional team leadership skills assessments. Outcome-driven been extensively used train students image-centric disciplines such as radiology, ultrasound, echocardiography, pathology. Allowing trainees first apply diagnostic decision support systems (DDSS) under simulated conditions leads improved accuracy, enhanced communication patients, safer triage decisions, outcomes response teams. However, issue bias, hallucinations, uncertainty emergent properties may undermine faith professionals they see deployed clinical setting participating judgments. Also, demands ensuring our curricula burdens on assets faculty adapt rapidly changing technological landscape. Nevertheless, introduction increased emphasis virtual reality platforms, thereby improving availability self-directed learning making available 24/7, along personalized evaluations customized coaching. Yet, caution must exercised concerning AI, especially society's earlier, delayed, muted responses inherent dangers social media raise serious questions about whether American government its citizenry can anticipate security privacy guardrails that need protect practitioners, students, patients.

Language: Английский

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Digital twins as global learning health and disease models for preventive and personalized medicine

Genome Medicine, Journal Year: 2025, Volume and Issue: 17(1)

Published: Feb. 7, 2025

Abstract Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands genes across multiple cell types organs. Disease progression can vary between patients over time, influenced by genetic environmental factors. To address this challenge, digital twins have emerged as promising approach, led to international initiatives aiming at clinical implementations. Digital are virtual representations health disease processes that integrate real-time data simulations predict, prevent, personalize treatments. Early applications DTs shown potential in areas like artificial organs, cancer, cardiology, hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes biological scales; (2) developing computational methods into DTs; (3) prioritizing mechanisms therapeutic targets; (4) creating interoperable DT systems learn each other; (5) designing user-friendly interfaces for clinicians; (6) scaling technology globally equitable access; (7) addressing ethical, regulatory, financial considerations. Overcoming these hurdles could pave way more predictive, preventive, personalized medicine, potentially transforming delivery improving outcomes.

Language: Английский

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Advancing Healthcare with Digital Twins: A Meta-Review of Applications and Implementation Challenges (Preprint)

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e69544 - e69544

Published: Jan. 24, 2025

Background Digital twins (DTs) are digital representations of real-world systems, enabling advanced simulations, predictive modeling, and real-time optimization in various fields, including health care. Despite growing interest, the integration DTs care faces challenges such as fragmented applications, ethical concerns, barriers to adoption. Objective This study systematically reviews existing literature on DT applications with three objectives: (1) map primary (2) identify key limitations, (3) highlight gaps that can guide future research. Methods A meta-review was conducted a systematic fashion, adhering PRISMA-ScR (Preferred Reporting Items for Systematic Reviews Meta-Analyses extension Scoping Reviews) guidelines, included 25 published between 2021 2024. The search encompassed 5 databases: PubMed, CINAHL, Web Science, Embase, PsycINFO. Thematic synthesis used categorize stakeholders, Results total 3 were identified: personalized medicine, operational efficiency, medical While current diagnostics, patient-specific treatment hospital resource optimization, remain their early stages development, they significant potential DTs. Challenges include data quality, issues, socioeconomic barriers. review also identified scalability, interoperability, clinical validation. Conclusions hold transformative care, providing individualized accelerated However, adoption is hindered by technical, ethical, financial Addressing these issues requires interdisciplinary collaboration, standardized protocols, inclusive implementation strategies ensure equitable access meaningful impact.

Language: Английский

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Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation

Frontiers in Digital Health, Journal Year: 2022, Volume and Issue: 4

Published: Oct. 6, 2022

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict prevention, diagnosis, and treatment individual patients. This be realized based on advances high performance computing, computational modeling, an expanding repertoire of observational data across multiple scales modalities. In 2020, the US National Cancer Institute, Department Energy, through trans-disciplinary research community at intersection advanced computing research, initiated team science collaborative projects explore development implementation predictive Patient Digital Twins. Several diverse pilot were launched provide key insights into important features this emerging landscape determine requirements for adoption twins. Projects included exploring approaches using large cohort perform deep phenotyping plan treatments level, prototyping self-learning twin platforms, adaptive monitor response resistance, developing methods integrate fuse observations scales, personalizing type. Collectively these efforts have yielded increased opportunities challenges facing helped define path forward. Given growing interest twins, manuscript provides valuable early progress report several CPDT commenced common, overall aims, progress, lessons learned directions that increasingly involve broader community.

Language: Английский

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MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Cancer Research, Journal Year: 2022, Volume and Issue: 82(18), P. 3394 - 3404

Published: Aug. 1, 2022

Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods improve targeting evaluation of responses therapy in this disease are needed. Here, we integrate quantitative MRI data with biologically based mathematical modeling accurately predict the response TNBC neoadjuvant systemic (NAST) on an individual basis. Specifically, 56 patients enrolled ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) then paclitaxel for NAST, where dynamic contrast-enhanced diffusion-weighted were acquired before treatment after two four cycles A/C. A model was established characterize tumor cell movement, proliferation, treatment-induced death. Two frameworks investigated using: (i) images A/C calibration predicting status A/C, (ii) before, cycles, following NAST. For Framework 1, concordance correlation coefficients between predicted measured patient-specific, post-A/C changes cellularity volume 0.95 0.94, respectively. 2, achieved area under receiver operator characteristic curve 0.89 (sensitivity/specificity = 0.72/0.95) differentiating pathological complete (pCR) from non-pCR, which statistically superior (P < 0.05) value 0.78 0.72/0.79) by Overall, successfully captured spatiotemporal dynamics providing highly accurate predictions NAST response.

Language: Английский

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Harnessing progress in radiotherapy for global cancer control

Nature Cancer, Journal Year: 2023, Volume and Issue: 4(9), P. 1228 - 1238

Published: Sept. 25, 2023

Language: Английский

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Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

Frontiers in Artificial Intelligence, Journal Year: 2023, Volume and Issue: 6

Published: Oct. 11, 2023

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. illustrate the as an enabler anticipatory personalized treatment that accounts uncertainties in underlying tumor biology high-grade gliomas, where heterogeneity response standard-of-care (SOC) radiotherapy contributes sub-optimal patient outcomes. The twin is initialized through prior distributions derived from population-level data literature mechanistic model's parameters. Then using Bayesian model calibration assimilating patient-specific magnetic resonance imaging data. calibrated used propose regimens by solving multi-objective risk-based optimization under uncertainty problem. solution leads suite of exhibiting varying levels trade-off between two competing objectives: (i) maximizing control (characterized minimizing risk volume growth) and (ii) toxicity radiotherapy. proposed framework illustrated generating

Language: Английский

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