The Future of Mathematical Oncology in the Age of AI

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UDC.coleccionInvestigación
UDC.grupoInvGrupo de Métodos Numéricos en Enxeñaría (GMNI)
dc.contributor.authorRockne, Russell C.
dc.contributor.authorLorenzo, Guillermo
dc.date.accessioned2026-05-25T12:26:01Z
dc.date.available2026-05-25T12:26:01Z
dc.date.issued2026-01
dc.description.abstract[Abstract]: This perspective article discusses emerging advances at the interface of mechanistic modeling and data-driven machine learning, highlighting opportunities for AI to accelerate discovery, improve predictive modeling, and enhance clinical decision-making. We address critical limitations of current AI approaches and propose a perspective on a future where AI augments mechanistic rigor, clinical relevance, and human creativity under the umbrella of a redefined understanding of Mathematical Oncology.
dc.description.sponsorshipThe authors acknowledge the Beckman Research Institute at City of Hope for financial support.
dc.identifier.citationRockne, R.C., Andersen, M., Anderson, A.R.A. et al. The future of mathematical oncology in the age of AI. npj Syst Biol Appl 12, 22 (2026). https://doi.org/10.1038/s41540-026-00656-9
dc.identifier.doi10.1038/s41540-026-00656-9
dc.identifier.issn2056-7189
dc.identifier.urihttps://hdl.handle.net/2183/48368
dc.language.isoeng
dc.publisherNature Research
dc.relation.urihttps://doi.org/10.1038/s41540-026-00656-9
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectComputational biology
dc.subjectBioinformatics
dc.subjectCancer
dc.subjectMathematical oncology
dc.titleThe Future of Mathematical Oncology in the Age of AI
dc.typereview
dspace.entity.typePublication
relation.isAuthorOfPublication9f720523-a782-4a42-995e-32753b82a0b5
relation.isAuthorOfPublication.latestForDiscovery9f720523-a782-4a42-995e-32753b82a0b5

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