Challenges and opportunities for digital twins in precision medicine: a complex systems perspective
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929345282441216 |
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| author | De Domenico, Manlio Allegri, Luca Caldarelli, Guido d'Andrea, Valeria Di Camillo, Barbara Rocha, Luis M. Rozum, Jordan Sbarbati, Riccardo Zambelli, Francesco |
| author_facet | De Domenico, Manlio Allegri, Luca Caldarelli, Guido d'Andrea, Valeria Di Camillo, Barbara Rocha, Luis M. Rozum, Jordan Sbarbati, Riccardo Zambelli, Francesco |
| contents | The adoption of digital twins (DTs) in precision medicine is increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. However, the reliance on black-box predictive models, which utilize large datasets, presents limitations that could impede the broader application of DTs in clinical settings. We argue that hypothesis-driven generative models, particularly multiscale modeling, are essential for boosting the clinical accuracy and relevance of DTs, thereby making a significant impact on healthcare innovation. This paper explores the transformative potential of DTs in healthcare, emphasizing their capability to simulate complex, interdependent biological processes across multiple scales. By integrating generative models with extensive datasets, we propose a scenario-based modeling approach that enables the exploration of diverse therapeutic strategies, thus supporting dynamic clinical decision-making. This method not only leverages advancements in data science and big data for improving disease treatment and prevention but also incorporates insights from complex systems and network science, quantitative biology, and digital medicine, promising substantial advancements in patient care. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_09649 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Challenges and opportunities for digital twins in precision medicine: a complex systems perspective De Domenico, Manlio Allegri, Luca Caldarelli, Guido d'Andrea, Valeria Di Camillo, Barbara Rocha, Luis M. Rozum, Jordan Sbarbati, Riccardo Zambelli, Francesco Biological Physics Adaptation and Self-Organizing Systems Quantitative Methods The adoption of digital twins (DTs) in precision medicine is increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. However, the reliance on black-box predictive models, which utilize large datasets, presents limitations that could impede the broader application of DTs in clinical settings. We argue that hypothesis-driven generative models, particularly multiscale modeling, are essential for boosting the clinical accuracy and relevance of DTs, thereby making a significant impact on healthcare innovation. This paper explores the transformative potential of DTs in healthcare, emphasizing their capability to simulate complex, interdependent biological processes across multiple scales. By integrating generative models with extensive datasets, we propose a scenario-based modeling approach that enables the exploration of diverse therapeutic strategies, thus supporting dynamic clinical decision-making. This method not only leverages advancements in data science and big data for improving disease treatment and prevention but also incorporates insights from complex systems and network science, quantitative biology, and digital medicine, promising substantial advancements in patient care. |
| title | Challenges and opportunities for digital twins in precision medicine: a complex systems perspective |
| topic | Biological Physics Adaptation and Self-Organizing Systems Quantitative Methods |
| url | https://arxiv.org/abs/2405.09649 |