Personalizing Medicine for Fake Drug Prevention With AI-Driven Digital Twins

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Author(s): Andrew Chinonso Nwanakwaugwu (University of Salford, UK), Nneoma Andrew-Vitalis (University of Hertfordshire, UK), Peter Kwakpovwe (University of Salford, UK), Daniel Emakporuena (Wrexham University, UK)and Esther Eboesomi (University of Huddersfield, UK)
Copyright: 2025
Pages: 34
Source title: AI-Powered Digital Twins for Predictive Healthcare: Creating Virtual Replicas of Humans
Source Author(s)/Editor(s): Balasubramaniam S. (Kerala University of Digital Sciences, Innovation, and Technology, India)and Seifedine Kadry (Lebanese American University, Lebanon & Noroff University College, Norway)
DOI: 10.4018/979-8-3373-0538-7.ch010

Keywords: Computer Science & IT / Computer Simulation / Medical Information Science Reference / Medical Technologies

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Abstract

As the day unfolds, we can no longer overemphasize on the negative impact of fake drugs in under-developed countries and in the whole world at large. The healthcare system has greatly been negatively affected by the illegal production of counterfeit drugs which results compromised patient safety, waste of money for healthcare, and causing lack of trust on the healthcare system in place. In this chapter on personalising medicine for fake drug prevention with AI-driven Digital Twin, digital twins are emerging as powerful tools. The virtual patient models combine multimodal data, such as clinical, genomic, and imaging information, in order to simulate an individual's health trajectory and response to treatment.

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Personalizing Medicine for Fake Drug Prevention With AI-Driven Digital Twins

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