Virtual Clinical Trial Emulation with Generative AI Models

这项研究将应用新出现的生成性人工智能技术，通过使用合成数据实现虚拟临床试验仿真，从而改变生物医学和健康研究。它将克服随机对照试验(RCT)和观察性研究中的关键限制。随机对照试验一直被认为是评估治疗和药物的“黄金标准”。然而，他们远不能回答所有的临床问题。除了时间和成本的限制外，随机对照试验在推广其研究结果方面也有很大的局限性，因为它们的范围有限。在许多情况下，对真实患者进行随机对照试验在逻辑上具有挑战性或不道德，因为它们具有潜在的危害性。这留下了一个巨大的知识缺口，例如，我们在如何管理多种疾病方面的临床指南仍然非常有限。虽然观察性研究可以通过利用常规从现实世界收集的数据来克服随机对照试验面临的问题，但它们在人口、疾病和干预方面通常是不平衡的；数据中有大量的噪声和缺失的测量，我们需要花费很长时间和大量努力才能从数据中删除患者可识别的信息以保护隐私。更重要的是，在真实世界的临床病例中，治疗选择和结果可能取决于数据中未测量的因素，这可能使观察性研究无效。人工智能研究在创建新数据方面取得了很大进展。使用新的生成性人工智能模型，我们可以生成忠实地保持真实人群的统计属性的合成患者群体。与匿名化的真实数据相比，合成数据可以生成无限大的数据量，同时包含关于真实个体的“零”信息。因此，他们在克服数据保护和共享方面的法律障碍方面处于更有利的地位。更重要的是，使用合成数据的实验将允许临床研究人员进行“虚拟试验”，以定量了解治疗及其效果之间的因果关系。这将使假设性治疗的预测和比较能够寻求目前无法在真实试验中回答的重要研究问题的答案。这项大胆而及时的研究的成功将带来一种格局变化，通过拓宽其研究议程，解放其限制，节省成本和时间，从而为未来的生物医学和健康研究带来革命性的变化，通过加快新的治疗发现时间表，为医疗保健带来重大好处，解决老年人口和多种疾病日益复杂的医疗保健格局，并改变监管和政策制定过程。

This research will apply newly emerging generative AI technology to transform biomedical and health research by enabling virtual clinical trial emulation with synthetic data. It will overcome key limitations in both Randomised Controlled Trials (RCTs) and observational studies. RCTs have long been considered as the "gold standard" to evaluate treatments and medicines. However, they are far from being able to answer all clinical questions. In addition to time and cost constraints, RCTs have significant limitations to generalise their findings as their scope is limited. In many situations conducting RCTs with real patients is logistically challenging or unethical due to their potentially harmful nature. This leaves a significant knowledge gap, for example, we still have very limited clinical guidelines about how to manage multi-morbidities. While observational studies can overcome the issues faced by RCTs by leveraging routinely collected data from real world, they are typically imbalanced across population, diseases and interventions; there are a significant amount of noise and missing measurements in the data, and we need lengthy time and significant effort to remove patient identifiable information from the data to protect privacy. More importantly, treatment choices and outcomes in real world clinical cases may depend on factors that are not measured within the data, which may invalidate the observational study. AI research has made great advances in creating new data. With new generative AI models, we can generate synthetic patient populations that faithfully preserve the statistical attributes of real populations. Compared with anonymised real data, synthetic data can be generated in unlimited volume while containing "zero" information about real individuals. Hence, they are in a much better position to overcome legal barriers in data protection and sharing. More importantly, experiments with synthetic data will allow clinical researchers to perform "virtual-trials" to gain quantitative insight into causal relations between treatment and its effect. This will enable prediction and comparison of hypothetical treatments to seek answers to important research questions that currently cannot be answered in real trials. The success of this adventurous and timely research will bring a landscape change to revolutionise future biomedical and health research by broadening its research agenda, liberating its restrictions, saving cost and time, leading to significant benefits to healthcare by speeding up new timelines for treatment discovery, addressing increasingly complex healthcare landscape in elderly population and multi-morbidity, and transforming regulatory and policy making process.