Related papers: Designing Value-Centered Consent Interfaces: A Mix…
Computer perception (CP) technologies (digital phenotyping, affective computing and related passive sensing approaches) offer unprecedented opportunities to personalize healthcare, but provoke concerns about privacy, bias and the erosion of…
As data is increasingly acknowledged as a highly valuable asset, much effort has been put into investigating inter-organisational data sharing, aiming at utilising the value of formerly unused data. Moreover, most researchers agree, that…
Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for…
Dementia care requires healthcare professionals to balance a patient's medical needs with a deep understanding of their personal needs, preferences, and emotional cues. However, current digital tools prioritise quantitative metrics over…
Online services are required to gain informed consent from users to collect, store and analyse their personal data, both intentionally divulged and derived during their use of the service. There are many issues with these forms: they are…
As Mixed Reality (MR) devices become increasingly popular across industries, they raise significant privacy and ethical concerns due to their capacity to collect extensive data on users and their environments. This paper highlights the…
Digital healthcare systems have revolutionized medical services, facilitating provider collaboration, enhancing diagnosis, and optimizing and improving treatments. They deliver superior quality, faster, reliable, and cost-effective…
Innovative HealthTech teams develop Artificial Intelligence (AI) systems in contexts where ethical expectations and organizational priorities must be balanced under severe resource constraints. While Responsible AI practices are expected to…
Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even…
Connected health is a multidisciplinary approach focused on health management, prioritizing pa-tient needs in the creation of tools, services, and treatments. This paradigm ensures proactive and efficient care by facilitating the timely…
Decision support tools enable improved decision-making for challenging decision problems by empowering stakeholders to process, analyze, visualize, and otherwise make sense of a variety of key factors. Their intentional design is a critical…
Ethical debates in AI have primarily focused on back-end issues such as data governance, model training, and algorithmic decision-making. Less attention has been paid to the ethical significance of front-end design choices, such as the…
Algorithms and technologies are essential tools that pervade all aspects of our daily lives. In the last decades, health care research benefited from new computer-based recruiting methods, the use of federated architectures for data…
The management of health data, from their gathering to their analysis, arises a number of challenging issues due to their highly confidential nature. In particular, this dissertation contributes to several security and privacy challenges…
The integration of continuous data from built-in sensors and Large Language Models (LLMs) has fueled a surge of "Sensor-Fused LLM agents" for personal health and well-being support. While recent breakthroughs have demonstrated the technical…
eHealth technologies have been increasingly used to foster proactive self-management skills for patients with chronic diseases. However, it is challenging to provide each user with their desired support due to the dynamic and diverse nature…
Understanding citizens' values in participatory systems is crucial for citizen-centric policy-making. We envision a hybrid participatory system where participants make choices and provide motivations for those choices, and AI agents…
This study reports the findings of qualitative interview sessions conducted with ICU clinicians for the co-design of a system user interface of an artificial intelligence (AI)-driven clinical decision support (CDS) system. This system…
Human-AI interfaces play a pivotal role in integrating clinicians' expertise with artificial intelligence to enhance both healthcare practice and research. However, designing effective interfaces in this domain remains a significant…
OBJECTIVE: To design and evaluate new pharmacogenomic (PGx) clinical decision support (CDS) alerts, built to adhere to PGx CDS design principles developed through socio-technical approaches. MATERIALS AND METHODS: Based on previously…