Credibility of in Silico Trial Technologies-A Theoretical Framing

Marco Viceconti, Miguel A. Juarez, Cristina Curreli, Marzio Pennisi, Giulia Russo, Francesco Pappalardo

Research output: Contribution to journalArticle

Abstract

Different research communities have developed various approaches to assess the credibility of predictive models. Each approach usually works well for a specific type of model, and under some epistemic conditions that are normally satisfied within that specific research domain. Some regulatory agencies recently started to consider evidences of safety and efficacy on new medical products obtained using computer modelling and simulation (which is referred to as In Silico Trials); this has raised the attention in the computational medicine research community on the regulatory science aspects of this emerging discipline. But this poses a foundational problem: in the domain of biomedical research the use of computer modelling is relatively recent, without a widely accepted epistemic framing for model credibility. Also, because of the inherent complexity of living organisms, biomedical modellers tend to use a variety of modelling methods, sometimes mixing them in the solution of a single problem. In such context merely adopting credibility approaches developed within other research communities might not be appropriate. In this paper we propose a theoretical framing for assessing the credibility of a predictive models for In Silico Trials, which accounts for the epistemic specificity of this research field and is general enough to be used for different type of models.

Original languageEnglish
Article number8884189
Pages (from-to)4-13
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume24
Issue number1
DOIs
Publication statusE-pub ahead of print - Oct 28 2019

Keywords

  • biomedical products
  • credibility of predictive models
  • In silico medicine
  • in silico trials
  • in silico-augmented clinical trials
  • regulatory science

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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