10143126EQUIVALENCE: Inclusive and representative evidence of medical device safety and efficacy based on synthetic virtual patients and in silico trials.ActiveCollaborative R&DInnovate UKProject EQUIVALENCE: Revolutionising Medical Device Testing with Virtual Populations Medical device testing is at a critical juncture. Traditional clinical trials, while valuable, are often limited in scope, time-consuming, and expensive. They may not adequately represent diverse populations, potentially leading to unforeseen complications when devices are widely deployed. EQUIVALENCE aims to address these challenges by refining, translating, and demonstrating the efficacy of a unique approach to creating high-quality, high-volume synthetic virtual populations for medical device testing. At the heart of EQUIVALENCE is a groundbreaking in-silico trials capability that leverages advanced generative AI modelling. This innovative technology creates synthetic virtual patients that closely mimic the diversity and complexity of real-world populations. Our aim is to demonstrate that these in-silico trials can replicate key safety and efficacy outcomes of conventional clinical trials, but at a fraction of the cost and time. The project focuses on three main objectives: 1. Refinement: We will enhance our existing virtual population generation technology, improving its accuracy, diversity, and scalability. This will involve integrating new data sources and advanced AI algorithms to create more comprehensive and representative virtual patient cohorts. 1. Translation: We will develop protocols and workflows to translate traditional clinical trial designs into in silico formats. This includes creating virtual analogues of medical interventions and defining virtual endpoints that correspond to real-world clinical outcomes. 1. Demonstration: We will conduct a series of virtual trials, paralleling completed real-world clinical trials for specific medical devices. By comparing the outcomes, we aim to validate the equivalence of our in-silico approach to traditional methods. The potential impact of EQUIVALENCE is substantial. Successful demonstration of this technology could accelerate medical device development, reduce costs, and most importantly, improve patient safety by enabling more comprehensive testing across diverse virtual populations. It could allow for the exploration of rare but critical scenarios that are difficult to encounter in traditional trials. Moreover, this approach aligns with ethical imperatives to reduce animal testing and minimise risk to human participants in early-stage trials. It also offers the potential to democratise medical device development, allowing smaller companies to conduct comprehensive testing that was previously only feasible for large corporations. EQUIVALENCE represents a significant step towards a future where medical devices are developed faster, tested more thoroughly, and designed with greater consideration for diverse patient populations. Our goal is to set a new standard in medical device testing, ultimately benefiting patients worldwide through safer, more effective medical technologies.