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Challenges and opportunities for digital twins in precision medicine from a complex systems perspective

npj Digital Medicine 2025 67 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 68 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Manlio De Domenico, Luca Allegri, Luca Allegri, Guido Caldarelli, Valeria d’Andrea, Barbara Di Camillo, Luís M. Rocha, Jordan C. Rozum, Riccardo Sbarbati, Riccardo Sbarbati, Francesco Zambelli

Summary

Researchers argue that digital twins — virtual computer models of individual patients — could transform personalized medicine by simulating how a person's biology responds to different treatments. Combining AI with detailed biological models allows doctors to test therapeutic strategies virtually before applying them in real clinical settings.

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.

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