TASH - Trustworthy Autonomous Systems for Health

随着人工智能（AI）的进步，该技术正被应用于与健康有关的问题。在卫生方面，我们如何评估这些系统的可信度？该项目将通过计算机科学和社会技术研究的结合，推动我们对可信赖的健康自主系统的理解，并由实际系统的实施推动。该项目将开发和扩展现有的自主系统，用于早期检测医院患者健康状况恶化，与现有广泛使用的基本预警评分（NEWS2）相比，使用机器学习临床恶化评分（CARDS）的安全性、可靠性和可接受性。使用该系统和现有的可信赖自治框架，该项目将首先进一步了解系统将部署到的医疗保健生态系统，以及其实施的实际、专业和社会障碍。然后，该项目将确定医疗自主可信系统所需的变化和扩展，从而开发可信系统框架，重点关注健康生态系统的具体需求，包括组织要求、临床交互、用户界面和监管机构等。

As advances are made in Artificial Intelligence (AI), the technology is being applied to health-related problems. How do we evaluate the trustworthiness of these systems in the health context? This project will push the boundaries of our understanding of trustworthy autonomous systems in health through a mix of computer science and sociotechnical research, driven by the implementation of a real system. The project will develop and expand an existing autonomous system for early detection of deteriorating health in hospital patients, using safety, reliability and acceptability of a machine learning clinical deterioration score (CARDS) in comparison to the existing extensively used basic Early Warning Score (NEWS2). Using this system, and existing trustworthy autonomous frameworks, the project will first develop a further understanding of the healthcare ecosystem into which the system will be deployed along with the practical, professional and societal hurdles to its implementation. The project will then identify variations and extensions required by medical autonomous trustworthy systems leading to the development of a framework for trustworthy systems, focused on the specific needs of the health ecosystem, including organizational requirements, clinical interactions, user interfaces and regulatory bodies, etc.