Abstract

Uncertainty quantification (UQ) is an essential aspect of computational modelling and statistical prediction. Multiple applications, including geophysics, climate science and aerospace engineering, incorporate UQ in the development and translation of new technologies. In contrast, the application of UQ to biological and healthcare models is understudied and suffers from several critical knowledge gaps. In an era of personalized medicine, patient-specific modelling, and , a lack of UQ understanding and appropriate implementation of UQ methodology limits the success of modelling and simulation in a clinical setting. The main contribution of our review article is to emphasize the importance and current deficiencies of UQ in the development of computational frameworks for healthcare and biological systems. As the introduction to the special issue on this topic, we provide an overview of UQ methodologies, their applications in non-biological and biological systems and the current gaps and opportunities for UQ development, as later highlighted by authors publishing in the special issue.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 1)'.

MeSH

Uncertainty, Natural Science Disciplines

Cited by

• Digital Twins for Personalized Medicine Require Epidemiological Data and Mathematical Modeling: Viewpoint. J Med Internet Res, 2025
• Preface to the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'. Philos Trans A Math Phys Eng Sci, 2025