Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.
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http://dx.doi.org/10.1038/s43588-024-00607-6 | DOI Listing |
NPJ Digit Med
January 2025
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA.
Digital twins in precision medicine provide tailored health recommendations by simulating patient-specific trajectories and interventions. We examine the critical role of Verification, Validation, and Uncertainty Quantification (VVUQ) for digital twins in ensuring safety and efficacy, with examples in cardiology and oncology. We highlight challenges and opportunities for developing personalized trial methodologies, validation metrics, and standardizing VVUQ processes.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Department of Physics and Astronomy "Galileo Galilei", University of Padua, Padova, Italy.
Digital twins (DTs) in precision medicine are increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. We argue that including mechanistic simulations that produce behavior based on explicitly defined biological hypotheses and multiscale mechanisms is beneficial. It enables the exploration of diverse therapeutic strategies and supports dynamic clinical decision-making through insights from network science, quantitative biology, and digital medicine.
View Article and Find Full Text PDFMinerva Obstet Gynecol
January 2025
Obstetrics and Gynecology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy.
Background: Vaginal delivery in twins is feasible but challenging. Successful vaginal delivery of a non-vertex second twin depends on knowledge of specific obstetrical maneuvers. Skill acquisition at the patient's bedside is difficult, making simulation training an integral part of obstetrics and gynecology residency programs.
View Article and Find Full Text PDFHandb Exp Pharmacol
January 2025
Genentech Inc, South San Francisco, CA, USA.
In this chapter, we envision the future of Quantitative Systems Pharmacology (QSP) which integrates closely with emerging data and technologies including advanced analytics, novel experimental technologies, and diverse and larger datasets. Machine learning (ML) and Artificial Intelligence (AI) will increasingly help QSP modelers to find, prepare, integrate, and exploit larger and diverse datasets, as well as build, parameterize, and simulate models. We picture QSP models being applied during all stages of drug discovery and development: During the discovery stages, QSP models predict the early human experience of in silico compounds created by generative AI.
View Article and Find Full Text PDFAnnu Rev Biomed Data Sci
January 2025
2Departments of Bioengineering and Genetics, Stanford University, Stanford, California, USA.
Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured clinical trial data, are rich sources of information with the potential to advance precision medicine and optimize patient care. However, real-world medical datasets have limited patient diversity and cannot simulate hypothetical outcomes, both of which are necessary for equitable and effective medical research. Fueled by recent advancements in machine learning, generative models offer a promising solution to these data limitations by generating enhanced synthetic data.
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