Which Framework to Use? A Systematic Review of Ethical Frameworks for the Screening or Evaluation of Health Technology Innovations

The ethics of artificial intelligence systems in healthcare and medicine: from a local to a global perspective, and back

Artificial intelligence systems (ai-systems) (e.g. machine learning, generative artificial intelligence), in healthcare and medicine, have been received with hopes of better care quality, more efficiency, lower care costs, etc. Simultaneously, these systems have been met with reservations regarding their impacts on stakeholders' privacy, on changing power dynamics, on systemic biases, etc. Fortunately, healthcare and medicine have been guided by a multitude of ethical principles, frameworks, or approaches, which also guide the use of ai-systems in healthcare and medicine, in one form or another. Nevertheless, in this article, I argue that most of these approaches are inspired by a local isolationist view on ai-systems, here exemplified by the principlist approach. Despite positive contributions to laying out the ethical landscape of ai-systems in healthcare and medicine, such ethics approaches are too focused on a specific local healthcare and medical setting, be it a particular care relationship, a particular care organisation, or a particular society or region. By doing so, they lose sight of the global impacts ai-systems have, especially environmental impacts and related social impacts, such as increased health risks. To meet this gap, this article presents a global approach to the ethics of ai-systems in healthcare and medicine which consists of five levels of ethical impacts and analysis: individual-relational, organisational, societal, global, and historical. As such, this global approach incorporates the local isolationist view by integrating it in a wider landscape of ethical consideration so to ensure ai-systems meet the needs of everyone everywhere.

From Pixels to Principles: A Decade of Progress and Landscape in Trustworthy Computer Vision

The rapid development of computer vision technologies and applications has brought forth a range of social and ethical challenges. Due to the unique characteristics of visual technology in terms of data modalities and application scenarios, computer vision poses specific ethical issues. However, the majority of existing literature either addresses artificial intelligence as a whole or pays particular attention to natural language processing, leaving a gap in specialized research on ethical issues and systematic solutions in the field of computer vision. This paper utilizes bibliometrics and text-mining techniques to quantitatively analyze papers from prominent academic conferences in computer vision over the past decade. It first reveals the developing trends and specific distribution of attention regarding trustworthy aspects in the computer vision field, as well as the inherent connections between ethical dimensions and different stages of visual model development. A life-cycle framework regarding trustworthy computer vision is then presented by making the relevant trustworthy issues, the operation pipeline of AI models, and viable technical solutions interconnected, providing researchers and policymakers with references and guidance for achieving trustworthy CV. Finally, it discusses particular motivations for conducting trustworthy practices and underscores the consistency and ambivalence among various trustworthy principles and technical attributes.

The Ethics of Electronic Tracking Devices in Dementia Care: An Interview Study with Developers

Wandering is a symptom of dementia that can have devastating consequences on the lives of persons living with dementia and their families and caregivers. Increasingly, caregivers are turning towards electronic tracking devices to help manage wandering. Ethical questions have been raised regarding these location-based technologies and although qualitative research has been conducted to gain better insight into various stakeholders' views on the topic, developers of these technologies have been largely excluded. No qualitative research has focused on developers' perceptions of ethics related to electronic tracking devices. To address this, we performed a qualitative semi-structured interview study based on grounded theory. We interviewed 15 developers of electronic tracking devices to better understand how they perceive ethical issues surrounding the design, development, and use of these devices within dementia care. Our results reveal that developers are strongly motivated by moral considerations and believe that including stakeholders throughout the development process is critical for success. Developers felt a strong sense of moral obligation towards topics within their control and a weaker sense of moral obligation towards topics outside their control. This leads to a perceived moral boundary between development and use, where some moral responsibility is shifted to end-users.

Recentering responsible and explainable artificial intelligence research on patients: implications in perinatal psychiatry

In the setting of underdiagnosed and undertreated perinatal depression (PD), Artificial intelligence (AI) solutions are poised to help predict and treat PD. In the near future, perinatal patients may interact with AI during clinical decision-making, in their patient portals, or through AI-powered chatbots delivering psychotherapy. The increase in potential AI applications has led to discussions regarding responsible AI and explainable AI (XAI). Current discussions of RAI, however, are limited in their consideration of the patient as an active participant with AI. Therefore, we propose a patient-centered, rather than a patient-adjacent, approach to RAI and XAI, that identifies autonomy, beneficence, justice, trust, privacy, and transparency as core concepts to uphold for health professionals and patients. We present empirical evidence that these principles are strongly valued by patients. We further suggest possible design solutions that uphold these principles and acknowledge the pressing need for further research about practical applications to uphold these principles.

The ethical challenges of personalized digital health

Personalized digital health systems (pHealth) bring together in sharp juxtaposition very different yet hopefully complementary moral principles in the shared objectives of optimizing health care and the health status of individual citizens while maximizing the application of robust clinical evidence through harnessing powerful and often complex modern data-handling technologies. Principles brought together include respecting the confidentiality of the patient-clinician relationship, the need for controlled information sharing in teamwork and shared care, benefitting from healthcare knowledge obtained from real-world population-level outcomes, and the recognition of different cultures and care settings. This paper outlines the clinical process as enhanced through digital health, reports on the examination of the new issues raised by the computerization of health data, outlines initiatives and policies to balance the harnessing of innovation with control of adverse effects, and emphasizes the importance of the context of use and citizen and user acceptance. The importance of addressing ethical issues throughout the life cycle of design, provision, and use of a pHealth system is explained, and a variety of situation-relevant frameworks are presented to enable a philosophy of responsible innovation, matching the best use of enabling technology with the creation of a culture and context of trustworthiness.