Virtual Clinical Trial Emulation with Generative AI Models

该研究将应用新兴的生成式人工智能技术，通过使用合成数据进行虚拟临床试验模拟，改变生物医学和健康研究。它将克服随机对照试验（RCTs）和观察性研究中的关键局限性。长期以来，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，它们还远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发现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后勤上具有挑战性或不道德，因为它们具有潜在的有害性质。这留下了一个重大的知识缺口，例如，我们仍然有非常有限的关于如何管理多重疾病的临床指南。虽然观察性研究可以通过利用从现实世界中常规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、疾病和干预措施方面通常是不平衡的；数据中存在大量的噪声和缺失的测量值，我们需要花费大量的时间和精力从数据中删除患者可识别信息以保护隐私。更重要的是，现实世界临床病例的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。人工智能研究在创造新数据方面取得了巨大进展。有了新的生成式人工智能模型，我们可以生成合成的患者群体，忠实地保留真实群体的统计属性。与匿名的真实数据相比，合成数据可以无限量地生成，而真实个人的信息“零”。因此，它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”，以定量地了解治疗与其效果之间的因果关系。这将能够预测和比较假设的治疗方法，以寻求目前在实际试验中无法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个景观变化，通过扩大其研究议程，解放其限制，节省成本和时间，通过加快治疗发现的新时间表，解决老年人和多发病人群日益复杂的医疗保健景观，以及改变监管和政策制定过程，从而为未来的生物医学和健康研究带来革命性的变化。

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.