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A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy
This paper contributes the first human-centered observational study of a deep learning system deployed directly in clinical care with patients. Through field observations and interviews at eleven clinics across Thailand, we explored the expectations and realities that nurses encounter in bringing a deep learning model into their clinical practices. First, we outline typical eye-screening workflows and challenges that nurses experience when screening hundreds of patients. Then, we explore the expectations nurses have for an AI-assisted eye screening process. Next, we present a human-centered, observational study of the deep learning system used in clinical care, examining nurses’ experiences with the system, and the socio-environmental factors that impacted system performance. Finally, we conclude with a discussion around applications of HCI methods to the evaluation of deep learning algorithms in clinical environments.
A socio-technical framework for digital contact tracing
In their efforts to tackle the COVID-19 crisis, decision makers are considering the development and use of smartphone applications for contact tracing. Even though these applications differ in technology and methods, there is an increasing concern about their implications for privacy and human rights. Here we propose a framework to evaluate their suitability in terms of impact on the users, employed technology and governance methods.
Tools on Cybercrime & Electronic Evidence Empowering you
Welcome to the Search portal of the Cybercrime
The European Data Protection Supervisor, 2019 Annual Report a year of transition
With new legislation on data protection in the EU now in place, our greatest challenge moving into 2020 is to ensure that this legislation produces the promised results. This includes ensuring that new rules on ePrivacy remain firmly on the EU agenda. Awareness of the issues surrounding data protection and privacy and the importance of rotecting these fundamental rights is at an all time high and we cannot allow this momentum to decline.
Artificial Intelligence and Machine Learning in Software as a Medical Device: discussion Paper and Request for Feedback
Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. The FDA is considering a total product lifecycle-based regulatory framework for these technologies.
The need for a system view to regulate artificial intelligence/machine learning-based software as medical device
FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition
When Autonomous Vehicles Are Hacked, Who Is Liable?
Who might face civil liability if autonomous vehicles (AVs) are hacked to steal data or inflict mayhem, injuries, and damage? How will the civil justice and insurance systems adjust to handle such claims? RAND researchers addressed these questions to help those in the automotive, technology, legal, and insurance industries prepare for the shifting roles and responsibilities that the era of AVs may bring.
Standardisation in support of the Cybersecurity Certification
The document presents the value of the cybersecurity standardisation efforts for certification, the roles and responsibilities of Standards Developing Organisations (SDOs) in this context, and discusses various ways how standardisation can support efficiently the process of certification schemes creation by following a step by step methodology.
Artificial Intelligence in Society
“The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises. Yet, as AI applications are adopted around the world, their use can raise questions and challenges related to human values, fairness, human determination, privacy, safety and accountability, among others. This report helps build a shared understanding of AI in the present and near-term by mapping the AI technical, economic, use case and policy landscape and identifying major public policy considerations. It is also intended to help co-ordination and consistency with discussions in other national and international fora”. (OECD)