CHAI - EPSRC AI Hub for Causality in Healthcare AI with Real Data

CHAI - EPSRC AI 中心，利用真实数据研究医疗保健 AI 中的因果关系

• 批准号: EP/Y028856/1
• 负责人: Sotirios Tsaftaris
• 金额: $ 1311万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY028856%2F1
• 关键词: CHAI; EPSRC; AI; Hub; Causality

The current AI paradigm at best reveals correlations between model input and output variables. This falls short of addressing health and healthcare challenges where knowing the causal relationship between interventions and outcomes is necessary and desirable. In addition, biases and vulnerability in AI systems arise, as models may pick up unwanted, spurious correlations from historic data, resulting in the widening of already existing health inequalities. Causal AI is the key to unlock robust, responsible and trustworthy AI and transform challenging tasks such as early prediction, diagnosis and prevention of disease. The Causality in Healthcare AI with Real Data (CHAI) Hub will bring together academia, industry, healthcare, and policy stakeholders to co-create the next-generation of world-leading artificial intelligence solutions that can predict outcomes of interventions and help choose personalised treatments, thus transforming health and healthcare. The CHAI Hub will develop novel methods to identify and account for causal relationships in complex data. The Hub will be built by the community for the community, amassing experts and stakeholders from across the UK to 1) push the boundaries of AI innovation; 2) develop cutting-edge solutions that drive desperately needed efficiency in resource-constrained healthcare systems; and 3) cement the UK's standing as a next-gen AI superpower. The data complexity in heterogeneous and distributed environments such as healthcare exacerbates the risks of bias and vulnerability and introduces additional challenges that must be addressed. Modern clinical investigations need to mix structured and unstructured data sources (e.g. patient health records, and medical imaging exams) which current AI cannot integrate effectively. These gaps in current AI technology must be addressed in order to develop algorithms that can help to better understand disease mechanisms, predict outcomes and estimate the effects of treatments. This is important if we want to ensure the safe and responsible use of AI in personalised decision making.Causal AI has the potential to unearth novel insights from observational data, formalise treatment effects, assess outcome likelihood, and estimate 'what-if' scenarios. Incorporating causal principles is critical for delivering on the National AI Strategy to ensure that AI is technically and clinically safe, transparent, fair and explainable.The CHAI Hub will be formed by a founding consortium of powerhouses in AI, healthcare, and data science throughout the UK in a hub-spoke model with geographic reach and diversity. The hub will be based in Edinburgh's Bayes Centre (leveraging world-class expertise in AI, data-driven innovation in health applications, a robust health data ecosystem, entrepreneurship, and translation). Regional spokes will be in Manchester (expertise in both methods and translation of AI through the Institute for Data Science and AI, and Pankhurst Institute), London (hosted at KCL, representing also UCL and Imperial, leveraging London's rapidly growing AI ecosystem) and Exeter (leveraging strengths in philosophy of causal inference and ethics of AI).The hub will develop a UK-wide multidisciplinary network for causal AI. Through extended collaborations with industry, policymakers and other stakeholders, we will expand the hub to deliver next-gen causal AI where it is needed most. We will work together to co-create, moving beyond co-ideation and co-design, to co-implementation, and co-evaluation where appropriate to ensure fit-for-purpose solutions Our programme will be flexible, will embed trusted, responsible innovation and environmental sustainability considerations, will ensure that equality diversity and inclusion principles are reflected through all activities, and will ensure that knowledge generated through CHAI will continue to have real-world impact beyond the initial 60 months.

目前的人工智能范式最多只能揭示模型输入和输出变量之间的相关性。在了解干预措施和结果之间的因果关系是必要和可取的情况下，这不足以解决健康和保健方面的挑战。此外，人工智能系统中的偏见和脆弱性也会出现，因为模型可能会从历史数据中获取不必要的虚假相关性，从而导致已经存在的卫生不平等现象进一步扩大。因果人工智能是开启强大、负责任和值得信赖的人工智能，并改变早期预测、诊断和预防疾病等具有挑战性的任务的关键。医疗保健AI与真实数据的因果关系（CHAI）中心将汇集学术界，工业界，医疗保健和政策利益相关者，共同创建下一代世界领先的人工智能解决方案，可以预测干预措施的结果并帮助选择个性化治疗，从而改变健康和医疗保健。CHAI中心将开发新的方法来识别和解释复杂数据中的因果关系。该中心将由社区为社区建立，聚集来自英国各地的专家和利益相关者，以1)推动人工智能创新的界限；2)开发尖端解决方案，提高资源有限的医疗系统迫切需要的效率；3)巩固英国作为下一代人工智能超级大国的地位。医疗保健等异构和分布式环境中的数据复杂性加剧了偏见和脆弱性的风险，并引入了必须解决的其他挑战。现代临床调查需要混合结构化和非结构化数据源（例如患者健康记录和医学成像检查），而目前的人工智能无法有效地整合这些数据源。必须解决当前人工智能技术中的这些差距，以便开发有助于更好地了解疾病机制、预测结果和估计治疗效果的算法。如果我们想确保人工智能在个性化决策中的安全和负责任的使用，这一点很重要。因果人工智能有可能从观察数据中挖掘出新的见解，正式确定治疗效果，评估结果的可能性，并估计“假设”情景。纳入因果原则对于实施国家人工智能战略至关重要，以确保人工智能在技术和临床上安全、透明、公平和可解释。CHAI中心将由英国各地人工智能、医疗保健和数据科学领域的强大企业组成的创始联盟组成，采用具有地理覆盖范围和多样性的中心辐式模式。该中心将设在爱丁堡的贝叶斯中心（利用世界一流的人工智能专业知识，健康应用领域的数据驱动创新，强大的健康数据生态系统，创业和翻译）。区域演讲将在曼彻斯特（通过数据科学与人工智能研究所和潘克赫斯特研究所在人工智能方法和翻译方面的专业知识），伦敦（在KCL主持，代表伦敦大学学院和帝国理工学院，利用伦敦快速增长的人工智能生态系统）和埃克塞特（利用因果推理哲学和人工智能伦理方面的优势）。该中心将为因果人工智能开发一个全英国范围的多学科网络。通过与行业、政策制定者和其他利益相关者的广泛合作，我们将扩大该中心，在最需要的地方提供下一代因果人工智能。我们将共同努力，共同创造，超越共同构想和共同设计，在适当情况下共同实施和共同评估，以确保符合目的的解决方案。我们的项目将具有灵活性，将纳入可信、负责任的创新和环境可持续性考虑，将确保平等、多样性和包容原则体现在所有活动中。并将确保通过CHAI产生的知识在最初的60个月之后继续对现实世界产生影响。