Trustworthy Artificial Intelligence for Personalised Risk Assessment in Chronic Heart Failure

心血管疾病仍然是全世界死亡的主要原因；特别是，心力衰竭（HF）在临床实践中提出了复杂的挑战，因为它与病因、表现和风险以及其进展和轨迹随时间的变化有很大的差异。心衰的临床风险可以从心功能下降和定期住院，一直到心脏事件和死亡。因此，有必要针对每位心衰患者的风险状况量身定制个性化的医疗方法（即改变生活方式、药物、干预措施），从而优化临床结果。从多源心血管数据中提取的人工智能（AI）解决方案有可能剖析每个患者的精确特征，并在早期阶段预测其可能的轨迹。然而，由于一个共同而关键的限制，现有的人工智能方法距离临床转移和采用还有很长的路要走：它们的可信度和心脏病专家和患者的接受程度还没有达到。AI4HF将开发首个可信赖的人工智能解决方案，用于心衰患者的个性化风险评估和管理。该项目将建立在一套独特的大数据存储库、可信赖的人工智能方法、计算工具和欧盟资助的心脏病学主要项目的临床结果之上。为了测试稳健性、公平性、透明度、可用性和可转移性，在欧盟和国际上的高收入和中低收入国家的八个临床中心进行了验证。AI4HF将根据联盟成员制定的FUTURE-AI指南，为可信赖和合乎道德的人工智能开发和评估制定一个全面和标准化的方法框架。AI4HF将通过持续的多利益攸关方参与来实施，同时考虑到临床需求和患者偏好，以及社会伦理和监管观点。

Cardiovascular diseases remain the main cause of mortality worldwide; in particular, heart failure (HF) poses complex challenges in clinical practice, as it is associated with a significant variability in aetiologies, manifestations and risks, as well as in its progression and trajectories over time. Clinical risks of HF can vary from reduced cardiac function and regular hospitalisations, all the way to cardiac events and mortality. There is a need for a personalised medicine approach to tailor the care models (i.e. lifestyle changes, medications, interventions) to each HF patient’srisk profile and hence optimise the clinical outcomes. Artificial intelligence (AI)solutionstrained from multi-source cardiovascular data have the potential to dissect the precise characteristics of each patient and predict their likely trajectories at an early stage. However, existing AI methods remain a far distance from clinical transfer and adoption due to a common and key limitation: their trustworthiness and acceptance by cardiologists and patients alike have not been achieved. AI4HF will develop the first trustworthy AI solutions for personalised risk assessment and management of HF patients. The project will build on a unique set of big data repositories, trustworthy AI methods, computational tools and clinical results from major EU-funded projects in cardiology. To test robustness, fairness, transparency, usability and transferability, the validation with take place in eight clinical centres in both high- and low-to-middle-income countries in the EU and internationally. AI4HF will develop a comprehensive and standardised methodological framework for trustworthy and ethical AI development and evaluation based on the FUTURE-AI guidelines developed by the consortium members. AI4HF will be implemented through continuous multi-stakeholder engagement, taking into account clinical needs and patient preferences, as well as socio-ethical and regulatory perspectives.