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相关的问题。在健康方面，我们如何评估这些系统的可信性？这个项目将通过计算机科学和社会技术研究的结合，在真实系统的实施的推动下，推动我们对健康中值得信赖的自主系统的理解的界限。该项目将利用机器学习临床恶化评分(CARD)的安全性、可靠性和可接受性，与现有广泛使用的基本预警评分(新闻2)相比，开发和扩大现有的自主系统，以便及早发现医院患者的健康恶化。使用该系统和现有的值得信赖的自主框架，该项目将首先进一步了解该系统将部署到的医疗保健生态系统，以及实施该系统所面临的实际、专业和社会障碍。然后，该项目将确定医疗自主可信赖系统所需的变体和扩展，从而开发可信赖系统的框架，重点关注健康生态系统的特定需求，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.