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.

当前的AI范式充其量可以揭示模型输入和输出变量之间的相关性。在应对健康和医疗保健挑战的问题上，了解干预措施与结果之间的因果关系是必要且可取的。此外，AI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得不必要的，虚假的相关性，从而导致已经存在的健康不平等扩大。因果AI是解锁强大，负责和值得信赖的AI并改变具有挑战性的任务，例如早期预测，诊断和预防疾病的关键。与真实数据（CHAI）枢纽的医疗保健AI因果关系将汇集学术界，行业，医疗保健和政策利益相关者，以共同创建世界领先的人工智能解决方案的下一代，可以预测干预措施的结果并帮助选择个性化治疗方法，从而改造健康和医疗保健。 Chai Hub将开发新的方法来识别和说明复杂数据中的因果关系。该枢纽将由社区为社区建造，从英国各地积累专家和利益相关者至1）突破AI创新的界限； 2）开发最先进的解决方案，这些解决方案迫切需要在资源受限的医疗系统中迫切需要效率； 3）巩固英国作为下一代AI超级大国的地位。诸如医疗保健等异质和分布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。现代临床研究需要将当前AI无法有效整合的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）混合。必须解决当前AI技术中的这些差距，以开发可以帮助更好地理解疾病机制，预测结果并估算治疗效果的算法。如果我们想确保在个性化决策中对AI的安全和负责任的使用。CausalAI有可能从观察数据中发掘出新颖的见解，正式化治疗效果，评估结果可能性并估算“ What-if”场景。纳入因果原理对于交付国家AI策略至关重要，以确保AI在技术和临床上是安全，透明，公平和可解释的。柴轮将由AI，Healthcare和Data Science的Powerhouses创始财团组成，在整个英国的数据科学领域，具有地理范围和多样性的Hub Spoke模型。该枢纽将位于爱丁堡的贝叶斯中心（利用世界一流的专业知识，在健康应用程序中以数据为驱动的创新，强大的健康数据生态系统，企业家精神和翻译）。区域发言人将在曼彻斯特（通过数据科学和AI研究所的方法和翻译方面的专业知识和翻译专业知识，以及Pankhurst Institute），伦敦（KCL主持，也代表UCL和帝国，在伦敦迅速发展的AI生态系统）和Exeter（在伦敦迅速发展的AI生态系统中）和exeter（在Mullder Ispor的哲学上都在CAUSAL COSLEIST和ELLEDISP的哲学上促进了AIPORPORY andibriisr of Causism of aim of a a a a a a。因果AI。通过与行业，政策制定者和其他利益相关者的扩展合作，我们将扩展枢纽，以提供最需要的下一代AI。 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个月。