Patient Preferences Concerning Humanoid Features in Healthcare Robots.

In this paper, I argue that patient preferences concerning human physical attributes associated with race, culture, and gender should be excluded from public healthcare robot design. On one hand, healthcare should be (objective, universal) needs oriented. On the other hand, patient well-being (the aim of healthcare) is, in concrete ways, tied to preferences, as is patient satisfaction (a core WHO value). The shift toward patient-centered healthcare places patient preferences into the spotlight. Accordingly, the design of healthcare technology cannot simply disregard patient preferences, even those which are potentially morally problematic. A method for handling these at the design level is thus imperative. By way of uncontroversial starting points, I argue that the priority of the public healthcare system is the fulfillment of patients' therapeutic needs, among which certain potentially morally problematic preferences may be counted. There are further ethical considerations, however, which, taken together, suggest that the potential benefits of upholding these preferences are outweighed by the potential harms.

The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.

BACKGROUND: Despite continuous performance improvements, especially in clinical contexts, a major challenge of Artificial Intelligence based Decision Support Systems (AI-DSS) remains their degree of epistemic opacity. The conditions of and the solutions for the justified use of the occasionally unexplainable technology in healthcare are an active field of research. In March 2024, the European Union agreed upon the Artificial Intelligence Act (AIA), requiring medical AI-DSS to be ad-hoc explainable or to use post-hoc explainability methods. The ethical debate does not seem to settle on this requirement yet. This systematic review aims to outline and categorize the positions and arguments in the ethical debate. METHODS: We conducted a literature search on PubMed, BASE, and Scopus for English-speaking scientific peer-reviewed publications from 2016 to 2024. The inclusion criterion was to give explicit requirements of explainability for AI-DSS in healthcare and reason for it. Non-domain-specific documents, as well as surveys, reviews, and meta-analyses were excluded. The ethical requirements for explainability outlined in the documents were qualitatively analyzed with respect to arguments for the requirement of explainability and the required level of explainability. RESULTS: The literature search resulted in 1662 documents; 44 documents were included in the review after eligibility screening of the remaining full texts. Our analysis showed that 17 records argue in favor of the requirement of explainable AI methods (xAI) or ad-hoc explainable models, providing 9 categories of arguments. The other 27 records argued against a general requirement, providing 11 categories of arguments. Also, we found that 14 works advocate the need for context-dependent levels of explainability, as opposed to 30 documents, arguing for context-independent, absolute standards. CONCLUSIONS: The systematic review of reasons shows no clear agreement on the requirement of post-hoc explainability methods or ad-hoc explainable models for AI-DSS in healthcare. The arguments found in the debate were referenced and responded to from different perspectives, demonstrating an interactive discourse. Policymakers and researchers should watch the development of the debate closely. Conversely, ethicists should be well informed by empirical and technical research, given the frequency of advancements in the field.

Conflicts of Interest Anesthesia Practice: Relationships with Industry, Responsibility to the Health Care System, and Research Integrity.

Conflicts of interest (COIs) between clinical obligations and other roles and responsibilities occur throughout health care, including anesthetic practice. In some circumstances, these conflicts are unavoidable. This article describes some of the potential conflicts and explores approaches to managing them in 3 key areas: relationships with industry, responsibility to the health care system, and obligations in research. Although working with industry has many benefits, COIs may arise from financial arrangements, potential biases that may go unrecognized, prioritization of commercial interests and even guideline development.

Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias.

Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, and health equity.

Healthcare providers' advocacy approaches and ethical challenges in delivering healthcare to undocumented migrants: a scoping review.

Delivering healthcare to undocumented migrants presents a complex challenge for healthcare providers. Integrating advocacy efforts into their daily practices can be ambiguous in practical terms, stemming from the intricate task of addressing the health needs of this population while simultaneously advocating for their health rights within the constraints imposed on them. This study seeks to consolidate findings from literature regarding the advocacy approaches employed by healthcare providers and the correlated ethical challenges. We conducted a scoping review of qualitative literature by systematically searching four databases-PubMed/Medline, Embase, Cinahl, and Cochrane Library. For developing our search strategy, we employed the PICO (Population, Intervention, Comparison, Outcome) scheme. Our analysis followed the qualitative content analysis approach proposed by Graneheim and Lundman. 30 studies were included, revealing a cumulative total of 915 healthcare providers who were interviewed. A total of 30 themes emerged comprising 14 advocacy approaches and 16 ethical challenges. Healthcare providers made a deliberate choice to engage in advocacy, responding to injustices experienced by undocumented migrants. The spectrum of advocacy initiatives varied, encompassing voluntary participation in healthcare provision, empathetic understanding, and healthcare-focused strategies. We also identified numerous correlated ethical challenges, necessitating healthcare providers to strike a balance between their eagerness to assist and their professional competence, respect the autonomy of undocumented migrants, and establish trust with them. These findings not only offer practical guidance for healthcare providers to enhance accessibility to healthcare services for undocumented migrant patients but also foster awareness of the ethical challenges that may arise in their advocacy roles.

Clinician voices on ethics of LLM integration in healthcare: a thematic analysis of ethical concerns and implications.

OBJECTIVES: This study aimed to explain and categorize key ethical concerns about integrating large language models (LLMs) in healthcare, drawing particularly from the perspectives of clinicians in online discussions. MATERIALS AND METHODS: We analyzed 3049 posts and comments extracted from a self-identified clinician subreddit using unsupervised machine learning via Latent Dirichlet Allocation and a structured qualitative analysis methodology. RESULTS: Analysis uncovered 14 salient themes of ethical implications, which we further consolidated into 4 overarching domains reflecting ethical issues around various clinical applications of LLM in healthcare, LLM coding, algorithm, and data governance, LLM's role in health equity and the distribution of public health services, and the relationship between users (human) and LLM systems (machine). DISCUSSION: Mapping themes to ethical frameworks in literature illustrated multifaceted issues covering transparent LLM decisions, fairness, privacy, access disparities, user experiences, and reliability. CONCLUSION: This study emphasizes the need for ongoing ethical review from stakeholders to ensure responsible innovation and advocates for tailored governance to enhance LLM use in healthcare, aiming to improve clinical outcomes ethically and effectively.

Sociopolitical, clinical, and ethical challenges of sustainable fracture care in Haiti.

INTRODUCTION: In low- and middle-income countries such as Haiti, musculoskeletal injuries are the leading cause of morbidity and mortality. Untreated injuries can contribute to decreased mobility, leading to disability and reduced productivity for individuals. The accessibility of timely fracture care poses a substantial challenge in Haiti, where socioeconomic instability and recent surges in gang violence exacerbate an already strained healthcare infrastructure. This manuscript delves into the intricate barriers to sustainable fracture care in Haiti, shedding light on the sociopolitical landscape and clinical challenges that influence the delivery of orthopedic services. ETHICAL DISCUSSION: The ethical considerations in providing fracture care in Haiti are multifaceted, including classic medical principles, self-preservation in the face of violence, issues of justice in resource and service allocation, and concerns of nonmaleficence in the context of international volunteers. These ethical dilemmas arise from the complex interplay of limited resources, the dangers posed by the current sociopolitical climate, and the involvement of international aid in a vulnerable healthcare system. CONCLUSION: To address the clinical and ethical conflicts of providing fracture care in Haiti, solutions include education and training of Haitian orthopedic surgeons, capacity building of healthcare facilities, and establishing ethical standards for international volunteers. This comprehensive approach is vital for advancing sustainable fracture care in Haiti and other resource-limited settings.

Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist.

The widespread use of Chat Generative Pre-trained Transformer (known as ChatGPT) and other emerging technology that is powered by generative artificial intelligence (GenAI) has drawn attention to the potential ethical issues they can cause, especially in high-stakes applications such as health care, but ethical discussions have not yet been translated into operationalisable solutions. Furthermore, ongoing ethical discussions often neglect other types of GenAI that have been used to synthesise data (eg, images) for research and practical purposes, which resolve some ethical issues and expose others. We did a scoping review of the ethical discussions on GenAI in health care to comprehensively analyse gaps in the research. To reduce the gaps, we have developed a checklist for comprehensive assessment and evaluation of ethical discussions in GenAI research. The checklist can be integrated into peer review and publication systems to enhance GenAI research and might be useful for ethics-related disclosures for GenAI-powered products and health-care applications of such products and beyond.

What Are Epidemiological Foundations for Integrating Legal Services Into Health Care Settings?

Medical-legal partnerships vary widely in how they are structured and use data to inform service delivery. Epidemiological data on certain chronic conditions' prevalence, the incidence of potentially preventable morbidity, and health-harming legal factors also influence approaches to care. This article draws on a pediatric example of how data-driven medical care complements data-driven legal care. This article also considers medical and public health ethical frameworks to guide protected information sharing, promote optimal service delivery, and achieve the best possible medical-legal outcomes.

KPHA 2023 should explicitly include state accountability: Response to Fernandez et al.

The commentary "Public health ethics and the Kerala Public Health Act, 2023" published on January 27, 2024 in the Indian Journal of Medical Ethics (IJME) has received a response from members of the State Health Systems Resource Centre and Government Medical College, Malappuram, Kerala. They explain that the Kerala Public Health Act (KPHA) is a legal document and not required to explicitly include accountability mechanisms and social obligations of the state. Given the very real danger of state over-reach as was evident during the Covid pandemic, these checks and balances should, in fact, be non-negotiable. The position of KPHA on healthcare of migrant workers and patients with tuberculosis goes against existing public healthcare principles. There is therefore a need to revisit the Act to explicitly include state accountability.

Under siege: The role of settler colonialism in targeting healthcare systems.

This article looks at the effects of armed conflict on healthcare systems in Gaza at the intersection of international humanitarian aid, settler colonialism and the ethics of war. Since October 7, 2023, there has been a systemic assault on the health services in Gaza, rooted in colonial expansion. I begin with an overview of human rights and the concept of medical neutrality. This is followed by biopolitics within Gaza and the contradictions in international law regarding the ethics of war. Explained through the lens of historical revisionism, postcolonial theory and biopolitics, I attempt to highlight how healthcare systems are increasingly becoming targets of armed conflict in Gaza as a war strategy.

AI ethics in healthcare.

Artificial Intelligence (AI) holds promise in improving diagnostics and treatment. Likewise, AI is anticipated to mitigate the impacts of staff shortages in the healthcare sector. However, realising the expectations placed on AI requires a substantial effort involving patients and clinical domain experts. Against this setting, this review examines ethical challenges related to the development and implementation of AI in healthcare. Furthermore, we introduce and discuss various approaches, guidelines, and standards that proactively aim to address ethical challenges.

Conscience, Caricatures, and Catholic Identities.

Catholic health care is often viewed as antithetical to secular conceptions of autonomy. This view can engender calls to protect "choice" in Catholic facilities. However, this view is built on a fundamental misunderstanding of the Ethical and Religious Directives for Catholic Health Care Services (ERDs). This commentary, which responds to "Principled Conscientious Provision: Referral Symmetry and Its Implications for Protecting Secular Conscience," by Abram Brummett et al., seeks to demonstrate the nuance of the ERDs as well as to address some of the challenges various Catholic identities have when interpreting and living out the ERDs so that all patients receive high-quality, compassionate care. By highlighting the Church's desire to protect all people at every stage, I hope to dispel the caricatures that often result from misunderstandings by Catholics and non-Catholics alike.

Responsibility Gap(s) Due to the Introduction of AI in Healthcare: An Ubuntu-Inspired Approach.

Due to its enormous potential, artificial intelligence (AI) can transform healthcare on a seemingly infinite scale. However, as we continue to explore the immense potential of AI, it is vital to consider the ethical concerns associated with its development and deployment. One specific concern that has been flagged in the literature is the responsibility gap (RG) due to the introduction of AI in healthcare. When the use of an AI algorithm or system results in a negative outcome for a patient(s), to whom can or should responsibility for that outcome be assigned? Although the concept of the RG was introduced in Anglo-American and European philosophy, this paper aims to broaden the debate by providing an Ubuntu-inspired perspective on the RG. Ubuntu, deeply rooted in African philosophy, calls for collective responsibility, and offers a uniquely forward-looking approach to address the alleged RG caused by AI in healthcare. An Ubuntu-inspired perspective can serve as a valuable guide and tool when addressing the alleged RG. Incorporating Ubuntu into the AI ethics discourse can contribute to a more ethical and responsible integration of AI in healthcare.