Traditional medical Artificial Intelligence models, approved for clinical use, restrict themselves to single-modal data e.g. images only, limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal Transformer Models in healthcare can effectively process and interpret diverse data forms such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks like USLME question banks and continue to improve with scale. However, the adoption of these advanced AI models is not without (...) challenges. While multimodal deep learning models like Transformers offer promising advancements in healthcare, their integration requires careful consideration of the accompanying ethical and environmental challenges. (shrink)

On 8th August 2019, Secretary of State for Health and Social Care, Matt Hancock, announced the creation of a £250 million NHS AI Lab. This significant investment is justified on the belief that transforming the UK’s National Health Service (NHS) into a more informationally mature and heterogeneous organisation, reliant on data-based and algorithmically-driven interactions, will offer significant benefit to patients, clinicians, and the overall system. These opportunities are realistic and should not be wasted. However, they may be missed (one may (...) recall the troubled Care.data programme) if the ethical challenges posed by this transformation are not carefully considered from the start, and then addressed thoroughly, systematically, and in a socially participatory way. To deal with this serious risk, the NHS AI Lab should create an Ethics Advisory Board and monitor, analyse, and address the normative and overarching ethical issues that arise at the individual, interpersonal, group, institutional and societal levels in AI for healthcare. (shrink)

Abstract: Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering unprecedented capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper explores the transformative impact of AI on the healthcare sector, examining how it enhances patient outcomes, improves the efficiency of medical practices, and introduces new ethical and operational challenges. By analyzing current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, this paper highlights the significant advancements AI has brought to the (...) field. However, the integration of AI also raises concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a comprehensive overview of these developments, offering insights into the future potential of AI in revolutionizing healthcare while addressing the ethical considerations that must accompany its adoption. The findings suggest that while AI holds the promise of transforming healthcare delivery, careful consideration of its implications is essential to ensure equitable and effective implementation. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, the (...) paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic (...) counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted. (shrink)

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and (...) Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

The research evaluates generative AI’s capabilities through a multi-phase framework, addressing how data synthesis, language models, and predictive algorithms contribute to sector-specific applications. In banking, the model assesses the impact of AI-driven chatbot interactions, credit risk assessment, and personalized financial services on customer experience and bank performance. Healthcare applications are explored through image synthesis for diagnostics, predictive modeling in patient care, and drug discovery simulations. The experimental setup is rigorously tested across metrics such as response accuracy, cost-effectiveness, and data privacy (...) to determine the benefits and potential risks associated with generative AI in these fields. (shrink)

Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for this tutorial are (...) the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. -/- Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems. (shrink)

Self-tracking-technologies can serve as a prominent example of how digital technologies put to test established practices, institutions, and structures of medicine and healthcare. While proponents emphasize the potentials, e.g., for individualized healthcare and new research data, opponents stress the risk that these technologies will reinforce gender-related inequalities. -/- While this has been made clear from—often intersectional—feminist perspectives since the introduction of such technologies, we aim to provide a queer-feminist perspective on self-tracking applications in healthcare by analyzing three concrete cases. In (...) this way, we want to show why such a perspective is helpful in assessing self-tracking technologies and how such a perspective might contribute to a queering of healthcare. -/- After a short introduction to self-tracking in healthcare and the corresponding risks of reproducing gender-related inequalities, we introduce queer bioethics as a methodology to examine structural discrimination and marginalization in bioethical research. We apply this framework in the analysis of three exemplary self-tracking applications from the areas of nutrition/fitness, reproductive health, and mental health. In the subsequent discussion of these results against the backdrop of already existing queer-sensitive technologies, we identify an equal access, a subversive design, an inclusive database, and a diversity-sensitive and discriminatory-critical definition of purposes as well as individualized usage possibilities as first possible starting points for a queering of self-tracking applications. Finally, we draw conclusions, how such a critical perspective on self-tracking applications might also contribute to a queering of the healthcare system at a structural level. (shrink)

Peer commentary on Char, Abràmoff & Feudtner (2020) target article: "Identifying Ethical Considerations for Machine Learning Healthcare Applications" .

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key (...) ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into leadership practices is rapidly transforming organizational dynamics and decision-making processes. This paper explores the ways in which AI enhances leadership effectiveness by providing data- driven insights, optimizing decision-making, and automating routine tasks. Additionally, it examines the challenges leaders face when adopting AI, including ethical considerations, potential biases in AI systems, and the need for upskilling. By analyzing current applications of AI in leadership and discussing future trends, this study aims to offer a (...) comprehensive overview of the opportunities AI presents for effective leadership, as well as the strategies required to address its associated challenges. (shrink)

rtificial Intelligence (AI) is swiftly becoming a fundamental element in modern healthcare, bringing unparalleled capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper delves into AI's transformative impact on the healthcare sector, highlighting how it enhances patient outcomes, boosts the efficiency of medical practices, and introduces new ethical and operational challenges. Through an analysis of current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, the paper underscores the significant advancements AI has introduced to (...) the field. However, the integration of AI also presents concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a thorough overview of these developments, offering insights into AI's future potential to revolutionize healthcare while addressing the ethical considerations necessary for its responsible adoption. The findings suggest that while AI has the potential to transform healthcare delivery, careful consideration of its implications is essential for ensuring its equitable and effective implementation. (shrink)

Technological developments involving robotics and artificial intelligence devices are being employed evermore in elderly care and the healthcare sector more generally, raising ethical issues and practical questions warranting closer considerations of what we mean by “care” and, subsequently, how to design such software coherently with the chosen definition. This paper starts by critically examining the existing approaches to the ethical design of care robots provided by Aimee van Wynsberghe, who relies on the work on the ethics of care by Joan (...) Tronto. In doing so, it suggests an alternative to their non-principled approach, an alternative suited to tackling some of the issues raised by Tronto and van Wynsberghe, while allowing for the inclusion of two orientative principles. Our proposal centres on the principles of autonomy and vulnerability, whose joint adoption we deem able to constitute an original revision of a bottom-up approach in care ethics. Conclusively, the ethical framework introduced here integrates more traditional approaches in care ethics in view of enhancing the debate regarding the ethical design of care robots under a new lens. (shrink)

One interesting aspect of the Hwang-case has been the way in which this affair was assessed by academic journals such as Nature. Initially, Hwang’s success was regarded as evidence for the detrimental effects of research ethics, slowing down the pace of research in Western countries. Eventually, however, Hwang’s debacle was seen as evidence for the importance of ethics in the life sciences. Ironically, it was concluded that the West maintains its prominence in science (as a global endeavour) precisely because it (...) has its ethics in place. Bioethics was now seen as an indispensable part of quality control. In this article, I will claim that the Hwang case rather reveals that there is no reason for complacency and that there are substantial challenges awaiting us. They have to do with major transformations in the way knowledge is produced and research in the life sciences is conducted (such as the increase in pace and scale, globalisation and the growing importance of ICT and bioinformation). These transformations call for a different kind of bioethics. The focus must shift from duties of autonomous researchers concerning visible research subjects (“micro-ethics”) to responsibilities of institutionalised research networks in managing and processing large amounts of bioinformation (“macro-ethics”). Concepts such as transparency, reliability and benefit-sharing will become more important than concepts such as informed consent. Basically, it is a resurgence of the tension between the Kantian and the Hegelian view of ethics. The contours of macro-ethics will be elaborated notably as it is emerging in bioethical debates over biobanking and genetic databanks. (shrink)

As national and state health care policy -making becomes contentious and complex, there is a need for a forum to debate and explore public concerns and values in health care, give voice to local citizens, to facilitate consensus among various stakeholders, and provide feedback and direction to health care institutions and policy makers. This paper explores the role that regional health care ethics committees can play and provides two contrasting examples of Networks involved in facilitation of public input into and (...) the development of health care policies and adoption of state-wide practices. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, (...) it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

Abstract: Generative AI is transforming the creative industries by redefining how art, music, and media are produced and experienced. This paper explores the profound impact of generative AI technologies, such as deep learning models and neural networks, on creative processes. By enabling artists, musicians, and content creators to collaborate with AI, these systems enhance creativity, speed up production, and generate novel forms of expression. The paper also addresses ethical considerations, including intellectual property rights, the role of human creativity in AI-assisted (...) production, and the potential risks of AI-generated content. As the convergence of AI and creativity deepens, this study highlights the opportunities and challenges posed by generative AI in revolutionizing the landscape of creative industries. (shrink)

Artificial Intelligence (AI) is rapidly transforming Human Resource Management (HRM) by enhancing the efficiency and effectiveness of key functions such as recruitment, performance management, and employee engagement. This paper explores the integration of AI technologies in HRM, focusing on their potential to revolutionize these critical areas. In recruitment, AI-driven tools streamline candidate sourcing, screening, and selection processes, leading to more accurate and unbiased hiring decisions. Performance management is similarly transformed, with AI enabling continuous, data-driven feedback and personalized development plans that (...) align with organizational goals. Furthermore, AI facilitates more engaging and tailored employee experiences by leveraging predictive analytics to anticipate and address employee needs. Despite these advancements, the paper also addresses the ethical considerations and challenges associated with AI in HRM, including data privacy concerns and the risk of over-reliance on automated systems. Through a comprehensive analysis, this paper aims to provide insights into how AI can be strategically leveraged to optimize HRM practices, while highlighting the need for a balanced approach that integrates human judgment with technological innovation. (shrink)

Vulnerability and politics are among the relevant and key topics of discussion in the Ethiopian healthcare context. Attempts by the formal bioethics structure in Ethiopia to deliberate on ethical issues relating to vulnerability and politics in healthcare have been limited, even though the informal analysis of bioethical issues has been present in traditional Ethiopian communities. This is reflected in religion, social values, and local moral underpinnings. Thus, the aim of this paper is to discuss the bioethical implications of vulnerability and (...) politics for healthcare in Ethiopia and to suggest possible ways forward. First, we will briefly introduce what has been done to develop bioethics as a field in Ethiopia and what gaps remain concerning its implementation in healthcare practice. This will give a context for our second and main task – analyzing the healthcare challenges in relation to vulnerability and politics and discussing their bioethical implications. In doing so, and since these two concepts are intrinsically broad, we demarcate their scope by focusing on specific issues such as poverty, gender, health governance, and armed conflicts. Lastly, we provide suggestions for the ways forward. (shrink)

The application of Artificial Intelligence (AI) in healthcare and epidemiology undoubtedly has many benefits for the population. However, due to its environmental impact, the use of AI can produce social inequalities and long-term environmental damages that may not be thoroughly contemplated. In this paper, we propose to consider the impacts of AI applications in medical care from the One Health paradigm and long-term global health. From health and environmental justice, rather than settling for a short and fleeting green honeymoon between (...) health and sustainability caused by AI, it should aim for a lasting marriage. To this end, we conclude by proposing that, in the upcoming years, it could be valuable and necessary to promote more interconnected health, call for environmental cost transparency, and increase green responsibility. (shrink)

Recent AI ethics has focused on applying abstract principles downward to practice. This paper moves in the other direction. Ethical insights are generated from the lived experiences of AI-designers working on tangible human problems, and then cycled upward to influence theoretical debates surrounding these questions: 1) Should AI as trustworthy be sought through explainability, or accurate performance? 2) Should AI be considered trustworthy at all, or is reliability a preferable aim? 3) Should AI ethics be oriented toward establishing protections for (...) users, or toward catalyzing innovation? Specific answers are less significant than the larger demonstration that AI ethics is currently unbalanced toward theoretical principles, and will benefit from increased exposure to grounded practices and dilemmas. (shrink)

Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether these (...) principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts. (shrink)

Abstract: In an era of rapid technological advancements, artificial intelligence (AI) has transformed surveillance systems, enhancing security capabilities across the globe. However, the deployment of AI-driven surveillance raises significant ethical concerns, particularly in balancing the need for security with the protection of individual privacy. This paper explores the ethical challenges posed by AI surveillance, focusing on issues such as data privacy, consent, algorithmic bias, and the potential for mass surveillance. Through a critical analysis of the tension between security and privacy, (...) this paper examines the implications of AI technologies on civil liberties and human rights. It also highlights the importance of establishing clear regulatory frameworks and ethical guidelines to ensure that AI surveillance systems operate within boundaries that respect individual freedoms. In navigating this delicate balance, we propose solutions that prioritize transparency, accountability, and the protection of privacy in the digital age. (shrink)

Abstract: Artificial Intelligence (AI) is revolutionizing organizational dynamics by reshaping both structures and processes. This paper explores how AI-driven innovations are transforming organizational frameworks, from hierarchical adjustments to decentralized decision-making models. It examines the impact of AI on various processes, including workflow automation, data analysis, and enhanced decision support systems. Through case studies and empirical research, the paper highlights the benefits of AI in improving efficiency, driving innovation, and fostering agility within organizations. Additionally, it addresses the challenges associated with AI (...) integration, such as resistance to change, ethical concerns, and data security issues. The paper concludes by offering insights into future trends and implications for organizations navigating the evolving landscape of AI-driven change. (shrink)

Commentary about the role of AI in bioethics for the 10th anniversary issue of AJOB: Empirical Bioethics.

Abstract: The integration of Artificial Intelligence (AI) into medical imaging marks a transformative advancement in healthcare, significantly enhancing diagnostic accuracy, efficiency, and patient outcomes. This paper delves into the application of AI technologies in medical image analysis, with a particular focus on techniques such as convolutional neural networks (CNNs) and deep learning models. We examine how these technologies are employed across various imaging modalities, including X-rays, MRIs, and CT scans, to improve disease detection, image segmentation, and diagnostic support. Furthermore, the (...) paper discusses the challenges associated with AI-driven medical imaging, such as data quality, model interpretability, and ethical considerations. By analyzing recent advancements and real-world case studies, this paper offers insights into the current landscape of AI in medical imaging and explores its potential future directions. The findings underscore the ongoing evolution of AI technologies and their pivotal role in advancing medical diagnostics and treatment strategies. (shrink)

The Netherlands was the first country to legalize euthanasia and physician-assisted suicide. This paper offers a first legal perspective on the prospects of using AI in the Dutch practice of euthanasia and physician-assisted suicide. It responds to the Regional Euthanasia Review Committees’ interest in exploring technological solutions to improve current procedures. The specific characteristics of AI – the capability to process enormous amounts of data in a short amount of time and generate new insights in individual cases – may for (...) example alleviate the increased workload of review committees due to the continuous increase of euthanasia cases. The paper considers three broad categories for the use of AI in the Dutch euthanasia practice: (1) the physician’s assessment of euthanasia requests, (2) the actual execution of euthanasia, and (3) the retrospective reviews of cases by the Regional Euthanasia Review Committees. Exploring the legal considerations around each avenue, both in the EU AI Act and the Dutch legal framework, this paper aims to facilitate the societal discussion on the role of technology in such deeply human decisions. This debate is equally relevant to other countries that legalized euthanasia (e.g. Belgium and Canada) or physician-assisted suicide (e.g. Switzerland and numerous states in the US). (shrink)

What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It’s also highly likely to impact on the organisational and business practices (...) of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare institutions. We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists. What does care look like when one is dealing with data as much as people? And, what weight should we give to the advice of machines in our own deliberations about medical decisions? (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate the patient’s outcomes (...) with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

Abstract: The convergence of nanotechnology and artificial intelligence (AI) represents a transformative frontier in modern science, with the potential to revolutionize multiple industries, particularly healthcare. Nanotechnology enables the manipulation of matter at the atomic and molecular scale, while AI offers sophisticated data analysis, pattern recognition, and decision-making capabilities. This paper explores the synergies between these two fields, focusing on their impact on medical diagnostics, targeted drug delivery, and personalized treatments. By leveraging AI's predictive power and nanotechnology's precision, healthcare can achieve (...) unprecedented levels of accuracy in disease detection and treatment, leading to improved patient outcomes. Beyond healthcare, this convergence extends to environmental monitoring, smart materials, and manufacturing, offering solutions to some of the world’s most pressing challenges. The paper also discusses the ethical considerations and potential risks of integrating AI with nanotechnology, emphasizing the need for responsible innovation. Together, AI and nanotechnology hold the promise of reshaping industries and improving quality of life on a global . (shrink)

While there is a steadily growing literature on epistemic injustice in healthcare, there are few discussions of the role that biomedical technologies play in harming patients in their capacity as knowers. Through an analysis of newborn and pediatric genetic and genomic sequencing technologies (GSTs), I argue that biomedical technologies can lead to epistemic injustice through two primary pathways: epistemic capture and value partitioning. I close by discussing the larger ethical and political context of critical analyses of GSTs and their broader (...) implications for just and equitable healthcare delivery. (shrink)

Discrimination is an important real-life issue that affects many individuals and groups. It is also a fruitful field of study that intersects several disciplines and methods. This Special Section brings together papers on discrimination and prioritization in healthcare from leading scholars in bioethics and closely related fields.

Abstract: The rapid advancement of artificial intelligence (AI) has catalyzed significant developments in autonomous systems, which are increasingly shaping diverse sectors including transportation, robotics, and industrial automation. This paper explores the evolution of AI technologies that underpin these autonomous systems, focusing on their capabilities, applications, and the challenges they present. Key areas of discussion include the technological innovations driving autonomy, such as machine learning algorithms and sensor integration, and the practical implementations observed in autonomous vehicles, drones, and robotic systems. Additionally, (...) the paper addresses critical challenges including safety, ethical concerns, and regulatory issues that influence the deployment and acceptance of autonomous technologies. By examining current trends and future prospects, this research aims to provide a comprehensive overview of how AI is transforming the landscape of autonomous systems and to identify key areas for future research and development. (shrink)

Why interdisciplinary research in AI is so important, according to Jurassic Park. -/- “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.” -/- I think this quote resonates with us now more than ever, especially in the world of technological development. The writers of Jurassic Park were years ahead of their time with this powerful quote. -/- As we build new technology, and we push on to see what can actually (...) be achieved there is an undertone of sales to whatever we build. The end product must be sold somewhere and to someone. This can derail any good intentions, just as building a resort full of dinosaurs was sold as a fun attraction, we see later in the film that it became a resort of terror. -/- In the field of AI we are certainly late to the party with ethics and regulation. Indeed, even existing modelling protocols have been, in many cases, circumvented or potentially ignored. This has led to a widening of the gap in the interdisciplinary field of AI. This is compounded by pop culture representations of AI and the Ethicist’s potential lack of knowledge surrounding technical progression in the field of AI. There are now seemingly two separate branches that ought to be in sync. The first branch is a group of Philosophers led by the Author of Superintelligence, Nick Bostrom, who believe that there is a singularity where AI takes over the world and starts to kill off humans. The second branch is the Technical Cohort who are led by companies such as Google and Deepmind. The remit of these developers is to see what can be developed and produced. Ultimately a separate sales team will determine what products can be sold. -/- We have seen multiple failures of AI due to lack of interdisciplinary discourse. Some cases include misallocated or cut off financial support and healthcare. This indicates the prevalence of issues that can grow and become ever more complex. -/- So, we have to ask ourselves two questions: One, do the Philosophers have a point? Two, does that point refer to humans programming AI in such a way that the destruction of the human race is its optimal aim in order to create a better future world? -/- Is the development of AI that different to an island of genetically engineered Dinosaurs? -/- The only solution, in my view, is interdisciplinary discourse. (shrink)

This thesis under the title “Public Health Policy in Resource Allocation: the Role of Ubuntu Ethics in Redressing Resource Disparity between Public and Private Healthcare in South Africa” explores health care disparities pertaining to resource allocation between public and private sector. It is of relevance and importance in South Africa where 54% of the population live on less than US$3 per day. Although the government has instituted certain changes aimed at transforming the public health care system, the resource allocation gap (...) between the two persists and remains wide. The research gives special attention to the role that Ubuntu ethics can play in redressing the resource allocation disparities between public and private health care sectors in South Africa, pointing out the existing gap in financing, as well as human resource between public and private health care sectors, which leads to poor health outcomes in public sector. Therefore, the problem is the prevalent inequality of resource allocation between private and public health care sectors. The analysis of policy gaps, as well as evaluating the cause of disparities in the health system is the goal of this research. The study endeavours to redress the disparities within the context of Ubuntu ethical principles and proposes a change in public policy so as to enhance healthcare for the benefit of the people. The study is philosophical and ethical, so the methodology used is discursive in nature, majorly focusing on critically reviewing existing literature, that is, the philosophical resources on Ubuntu ethics and justice theories as well as a range of government resources inclusive of public health policy documents. Social determinants of health and their impact in a South African context are discussed with the implication that health is not merely freedom from disease and or incapacity, but rather being well mentally, physically and socially. The attainment of a healthy status goes beyond ability to access healthcare services, and incorporates social determinants including but not limited to conditions in which people have been born into, stay, establish career and grow old in. It also includes the arrangements put in place to deal with illness and determine health. Issues of revenue, literacy, communal safety networks, vocation and conditions under-which vocation is undertaken, lack of work and vocation insecurity, early stages of human development, gender, cultural group, dietary insecurity, housing communal exclusion and disability all come into the picture as factors that determine health. But there is also discussion of the socio-economic status as contributors to health inequity wherein alcohol abuse, sugar intake, and smoking are identified as behavioural factors that complicate public health. One outstanding feature of this research is that it establishes a link between Ubuntu and public health policy. The researcher establishes that Ubuntu ethics is appropriate for not only discussing public morality, but also for formulating public policy that is morally tenable for South Africa, especially for the public health sector. This thesis calls for an application of Ubuntu in a proper context in order to enhance societal well-being for the benefit of many, if not all. The research proposes the fundamental principle of Ubuntu ethics in which the good of the community is as important as the good of every member of the community. Since Ubuntu ethics does not merely seek to fulfil what benefits the majority, as would be the case in a utilitarian society, the researcher advocates for the building of consensus on the common good, which should be understood to imply not just what is good for everyone in the community, but also what is good for the continued well-being of the community as a single unit. In this regard, the research finds it important that deliberate effort be made to integrate Ubuntu ethics into policies, citing the example of the National Health Insurance (NHI). The good health of all the members of a community is an assurance of the well-being and continuity of the whole national community. The research concludes that public health policy should integrate Ubuntu ethical values such as community, sharing, care, solidarity, identity, harmony, respect, and dignity into policy documents and legislation. (shrink)

Because of its non-representational nature, music has always had familiarity with computational and algorithmic methodologies for automatic composition and performance. Today, AI and computer technology are transforming systems of automatic music production from passive means within musical creative processes into ever more autonomous active collaborators of human musicians. This raises a large number of interrelated questions both about the theoretical problems of artificial musical creativity and about its ethical consequences. Considering two of the most urgent ethical problems of Musical AI (...) (music job replacement and machine musical authorship), we show in this essay the strict dependence of every form of acknowledgment of a moral and legal status to systems of automatic music production from the theoretical account of musical creativity by turns implicitly or explicitly adopted, arguing, on the basis of pragmatic reasons, for the necessity and the desirability of this acknowledgment. (shrink)

We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI development process. This approach addresses the limitations of what we call the "isolationist approach to AI bias," a trend in AI literature where biases are seen as separate occurrence linked to specific stages in an AI pipeline. Dealing with these multiple biases can trigger a sense of excessive overload in managing each potential bias individually or promote the (...) adoption of an uncritical approach to understanding the influence of biases in developers’ decision-making. The BNA fosters dialogue and a critical stance among developers, guided by external experts, using graphical representations to depict biased connections. To test the BNA, we conducted a pilot case study on the "waiting list” project, involving a small AI developer team creating a healthcare waiting list NPL model in Chile. The analysis showed promising findings: (i) the BNA aids in visualizing interconnected biases and their impacts, facilitating ethical reflection in a more accessible way; (ii) it promotes transparency in decision-making throughout AI development; and (iii) more focus is necessary on professional biases and material limitations as sources of bias in AI development. (shrink)

Abstract Aim: The COVID-19 infection is transmitted either by human-to-human contact, social-physical contact, and respiratory droplets or by touching items touched by the infected. This has triggered some conflicted behaviors such as stigma, violence, and opposite behavior applause. The aim of this study is to explore several newspaper articles about stigma, violence, or insensitive behavior against healthcare professionals and to analyze the reason for these behaviors during these COVID-19 pandemics. Method: The website of the Turkish Medical Association "Press Releases News" (...) and online newspaper articles have been scanned using keywords and have been classified and analyzed according to the content of articles between the periods of March 11 to April 28, 2020. This is a qualitative study with content analysis. No official ethical permission was obtained as the study was conducted through open access internet news sites. Result: 16 reports were selected from online reports that matched the keywords of the study. 13 of these reports included desensitization, violence, lack of precaution, stigmatization, and applause, and 3 reports included doctors’ statements. After being categorized, content analyses were conducted. Conclusion: This study revealed the necessity of a multi-faceted evaluation of the problems faced by healthcare professionals who are at the forefront of the COVID-19 outbreak. This study has a primary role in the detection, diagnosis, and treatment of the pandemic and reveals that doctors are trying to fulfill their duties in an ethical framework despite stigmatized behavior. Authorities should provide various supports to protect healthcare professionals before, during, and after the epidemic for the success of the COVID-19 outbreak struggle. (shrink)

The adoption of AI in the health sector has its share of benefits and harms to various stakeholder groups and entities. There are critical risks involved in using AI systems in the health domain; risks that can have severe, irreversible, and life-changing impacts on people’s lives. With the development of innovative AI-based applications in the medical and healthcare sectors, new types of risks emerge. To benefit from novel AI applications in this domain, the risks need to be managed in order (...) to protect the fundamental interests and rights of those affected. This will increase the level to which these systems become ethically acceptable, legally permissible, and socially sustainable. In this paper, we first discuss the necessity of AI risk management in the health domain from the ethical, legal, and societal perspectives. We then present a taxonomy of risks associated with the use of AI systems in the health domain called HART. HART mirrors the risks of a variety of different real-world incidents caused by use of AI in the health sector. Lastly, we discuss the implications of the taxonomy for different stakeholder groups and further research. (shrink)

Generative AI is reshaping sectors like banking and healthcare by enabling innovative applications such as personalized service offerings, predictive analytics, and automated content generation. In banking, generative AI drives customer engagement through tailored financial advice, fraud detection, and streamlined customer service. Meanwhile, in healthcare, it enhances medical imaging analysis, drug discovery, and patient diagnostics, significantly impacting patient care and operational efficiency. This paper presents an experimental study examining the implementation and effectiveness of generative AI in these sectors.

This paper explores some ways in which artificial intelligence (AI) could be used to improve human moral judgments in bioethics by avoiding some of the most common sources of error in moral judgment, including ignorance, confusion, and bias. It surveys three existing proposals for building human morality into AI: Top-down, bottom-up, and hybrid approaches. Then it proposes a multi-step, hybrid method, using the example of kidney allocations for transplants as a test case. The paper concludes with brief remarks about how (...) to handle several complications, respond to some objections, and extend this novel method to other important moral issues in bioethics and beyond. (shrink)

Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions and changing (...) behaviours. This article discusses how these AI-driven systems pose particular ethical challenges with regards to nudging. To confront these issues, the value sensitive design approach is adopted as a principled methodology that designers can adopt to design these systems to avoid harming and contribute to the social good. The AI for Social Good factors are adopted as the norms constraining maleficence. In contrast, higher-order values specific to AI, such as those from the EU High-Level Expert Group on AI and the United Nations Sustainable Development Goals, are adopted as the values to be promoted as much as possible in design. The use case of Amazon Alexa's Healthcare Skills is used to illustrate this design approach. It provides an exemplar of how designers and engineers can begin to orientate their design programs of these technologies towards the social good. (shrink)

Bioethicists’ views diverge public opinion on various ethical issues, particularly in healthcare. For instance, bioethicists generally oppose payment for organs and advocate for preventing death at any age, whereas the public is more supportive of organ payment and prioritizing younger patients. I offer four arguments on how best to view this divergence. (a) Bioethicists’ specialized training, objectivity, and reliance on research often lead to views that differ from those of the public, which may be less informed and more influenced by (...) biases. (b) Further, divergence between expert and public opinion is a normal and sometimes necessary aspect of bioethics. Divergence can lead to more informed and progressive ethical practices. (c) While public input is valuable, achieving meaningful engagement is difficult due to varying values, risk perceptions, and socioeconomic circumstances. Effective bioethics would balance expert knowledge with public input, aiming for policies that are both scientifically sound and democratically legitimate. (d) Maintaining public trust in bioethics does not require full alignment with public opinion. Instead, bioethicists should focus on educating the public and incorporating informed public perspectives into their analysis and recommendations. (shrink)

That AI will have a major impact on society is no longer in question. Current debate turns instead on how far this impact will be positive or negative, for whom, in which ways, in which places, and on what timescale. In order to frame these questions in a more substantive way, in this prolegomena we introduce what we consider the four core opportunities for society offered by the use of AI, four associated risks which could emerge from its overuse or (...) misuse, and the opportunity costs associated with its under use. We then offer a high-level view of the emerging advantages for organisations of taking an ethical approach to developing and deploying AI. Finally, we introduce a set of five principles which should guide the development and deployment of AI technologies. The development of laws, policies and best practices for seizing the opportunities and minimizing the risks posed by AI technologies would benefit from building on ethical frameworks such as the one offered here. (shrink)

The ethical issues raised by cybersecurity practices and technologies are of critical importance. However, there is disagreement about what is the best ethical framework for understanding those issues. In this paper we seek to address this shortcoming through the introduction of a principlist ethical framework for cybersecurity that builds on existing work in adjacent fields of applied ethics, bioethics, and AI ethics. By redeploying the AI4People framework, we develop a domain-relevant specification of five ethical principles in cybersecurity: beneficence, non-maleficence, autonomy, (...) justice, and explicability. We then illustrate the advantages of this principlist framework by examining the ethical issues raised by four common cybersecurity contexts: penetration testing, distributed denial of service attacks (DDoS), ransomware, and system administration. These case analyses demonstrate the utility of this principlist framework as a basis for understanding cybersecurity ethics and for cultivating the ethical expertise and ethical sensitivity of cybersecurity professionals and other stakeholders. (shrink)

AI is revolutionizing everyone’s life, and it is crucial that it does so in the right way. AI’s profound and far-reaching potential for transformation concerns the engineering of systems that have some degree of autonomous agency. This is epochal and requires establishing a new, ethical balance between human and artificial autonomy.

Techniques from behavioral economics—nudges—may help physicians increase pediatric vaccine compliance, but critics have objected that nudges can undermine autonomy. Since autonomy is a centrally important value in healthcare decision-making contexts, it counts against pediatric vaccination nudges if they undermine parental autonomy. Advocates for healthcare nudges have resisted the charge that nudges undermine autonomy, and the recent bioethics literature illustrates the current intractability of this debate. This article rejects a principle to which parties on both sides of this debate sometimes seem (...) committed: that nudges are morally permissible only if they are consistent with autonomy. Instead, I argue that, at least in the case of pediatric vaccination, some autonomy-undermining nudges may be morally justified. This is because parental autonomy in pediatric decision-making is not as morally valuable as the autonomy of adult patients, and because the interests of both the vaccinated child and other members of the community can sometimes be weighty enough to justify autonomy-infringing pediatric vaccination nudges. This article concludes with a set of worries about the effect of pediatric vaccination nudges on parent-physician relationships, and it calls on the American Academy of Pediatrics to draw on scientific and bioethics research to develop guidelines for the use of nudges in pediatric practice and, in particular, for the use of pediatric vaccination nudges. (shrink)

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the recent (...) work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2. (shrink)

In our article, Where the ethical action is, we argue that medical and ethical modes of thought are not different in kind but merely different aspects of a clinical situation. In response, Emmerich argues that in so doing, we neglect several important features of healthcare and medical education. Although we applaud the spirit of Emmerich’s response, we argue that his critique is an attempt at a general defence of the value of bioethical expertise in clinical practice, rather than a specific (...) critique of our account. (shrink)