The computational model lifecycle: Opportunities and challenges for computational medicine in the healthcare ecosystem

Abstract

Computational medicine promises significant advancements in healthcare, using physics-based simulations and artificial intelligence to optimise disease diagnosis, personalise treatment strategies and accelerate medical innovation. Biomedical research efforts are generating a growing number of computational models of human pathophysiology and medical treatments, with advanced applications in areas such as cardiovascular diseases, orthopaedics and cancer diagnosis. However, the widespread adoption of these models is hindered by technological and regulatory barriers. This article provides an overview of the potential impact, needs and challenges of the adoption of in silico medicine in the healthcare ecosystem, with a focus on initiatives to sustain this technology within the European Union's regulatory environment. The article introduces the concept of the 'computational model lifecycle' as a framework to describe the stages from academic research to pre-clinical and clinical applications, analysing key opportunities and challenges in translating these technologies at each stage. These challenges are associated with data management, standards for model credibility assessment, transparency of regulatory frameworks, and clinical integration. The article highlights European initiatives such as the European Health Data Space and the Virtual Human Twins Initiative, aimed at fostering the development and application of computational medicine in healthcare.

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