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Moral Laboratoriesis an engaging ethnography and a groundbreaking foray into the anthropology of morality. It takes us on a journey into the lives of African American families caring for children with serious chronic medical conditions, and it foregrounds the uncertainty that affects their struggles for a good life. Challenging depictions of moral transformation as possible only in moments of breakdown or in radical breaches from the ordinary, it offers a compelling portrait of the transformative powers embedded in day-to-day existence. From soccer fields to dinner tables, the everyday emerges as a moral laboratory for reshaping moral life. Cheryl Mattingly offers vivid and heart-wrenching stories to elaborate a first-person ethical framework, forcefully showing the limits of third-person renderings of morality.

Artificial intelligence (AI) is rapidly transforming health care, offering substantial advancements in patient care, clinical workflows, and nursing education. This umbrella review aims to evaluate the integration of AI into nursing practice and education, with a focus on ethical and social implications, and to propose evidence-based recommendations to support the responsible and effective adoption of AI technologies in nursing. We included systematic reviews, scoping reviews, rapid reviews, narrative reviews, literature reviews, and meta-analyses focusing on AI integration in nursing, published up to October 2024. A new search was conducted in January 2025 to identify any potentially eligible reviews published thereafter. However, no new reviews were found. Eligibility was guided by the Sample, Phenomenon of Interest, Design, Evaluation, Research type framework; databases (PubMed or MEDLINE, CINAHL, Web of Science, Embase, and IEEE Xplore) were searched using comprehensive keywords. Two reviewers independently screened records and extracted data. Risk of bias was assessed with Risk of Bias in Systematic Reviews (ROBIS) and A Measurement Tool to Assess Systematic Reviews, version 2 (AMSTAR 2), which we adapted for systematic and nonsystematic review types. A thematic synthesis approach, conducted independently by 2 reviewers, identified recurring patterns across the included reviews. The search strategy yielded 18 eligible studies after screening 274 records. These studies encompassed diverse methodologies and focused on nursing professionals, students, educators, and researchers. First, ethical and social implications were consistently highlighted, with studies emphasizing concerns about data privacy, algorithmic bias, transparency, accountability, and the necessity for equitable access to AI technologies. Second, the transformation of nursing education emerged as a critical area, with an urgent need to update curricula by integrating AI-driven educational tools and fostering both technical competencies and ethical decision-making skills among nursing students and professionals. Third, strategies for integration were identified as essential for effective implementation, calling for scalable models, robust ethical frameworks, and interdisciplinary collaboration, while also addressing key barriers such as resistance to AI adoption, lack of standardized AI education, and disparities in technology access. AI holds substantial promises for revolutionizing nursing practice and education. However, realizing this potential necessitates a strategic approach that addresses ethical concerns, integrates AI literacy into nursing curricula, and ensures equitable access to AI technologies. Limitations of this review include the heterogeneity of included studies and potential publication bias. Our findings underscore the need for comprehensive ethical frameworks and regulatory guidelines tailored to nursing applications, updated nursing curricula to include AI literacy and ethical training, and investments in infrastructure to promote equitable AI access. Future research should focus on developing standardized implementation strategies and evaluating the long-term impacts of AI integration on nursing practice and patient outcomes.

The integration of computational methods into psychiatry presents profound ethical challenges that extend beyond existing guidelines for AI and healthcare. While precision medicine and digital mental health tools offer transformative potential, they also raise concerns about privacy, algorithmic bias, transparency, and the erosion of clinical judgment. This article introduces the Integrated Ethical Approach for Computational Psychiatry (IEACP) framework, developed through a conceptual synthesis of 83 studies. The framework comprises five procedural stages – Identification, Analysis, Decision-making, Implementation, and Review – each informed by six core ethical values – beneficence, autonomy, justice, privacy, transparency, and scientific integrity. By systematically addressing ethical dilemmas inherent in computational psychiatry, the IEACP provides clinicians, researchers, and policymakers with structured decision-making processes that support patient-centered, culturally sensitive, and equitable AI implementation. Through case studies, we demonstrate framework adaptability to real-world applications, underscoring the necessity of ethical innovation alongside technological progress in psychiatric care.

Patients using e-pharmacies benefit substantially from their privacy and accessibility; however, they must also be aware of the health and safety risks. These risks can be mitigated through the implementation of robust legal and ethical frameworks. European pharmacists working in e-pharmacies are obligated to respect the legal frameworks governing the remote sale of medicines, as well as the rules of professional ethics, while considering the unique benefits and challenges of establishing online therapeutic relationships with patients. This Viewpoint paper aims to propose a comprehensive ethical framework for European pharmacists in e-pharmacies that promotes consistency and ensures patient rights while fostering professional integrity and responsibility. To this end, the codes of ethics of pharmacists in the European Union (EU) member states were explored to determine the extent to which they address e-pharmacies and to begin from their provisions the building of a shared ethical framework, supported by previously published findings. The ethical guidance provided by professional associations and other competent authorities of European pharmacists varies significantly across member states. In most EU member states, pharmacists’ codes of ethics do not contain rules addressing e-pharmacies. Only 8 EU member states have adopted rules guiding pharmacists’ conduct in e-pharmacies, providing detailed regulations aimed at safeguarding patients’ rights during the remote supply of medicines, health care products, and services. Considering these findings, the development of a common ethical framework for European pharmacists operating in e-pharmacies would be helpful for the development of ethics in their conduct on the internet. On the basis of the fundamental principles of biomedical ethics, we engaged in a reiterative process of reflection and discussion to develop an ethical framework tailored to the e-pharmacy context as our main contribution to knowledge. The proposed framework—representing a novel contribution to e-pharmacy ethics research—highlights ethical issues that should be incorporated into pharmacists’ codes of ethics or dedicated guidelines. These issues range from ensuring patient autonomy in selecting e-pharmacies to showing solidarity in the global e-pharmacy environment. Its novelty could be a major input for advancing e-pharmacy ethics. The following key messages are intended for pharmacists and their professional associations or other competent authorities interested in this field. Pharmacists’ codes of ethics should keep pace with the development of e-pharmacies to ensure they remain relevant and effective in guiding ethical conduct and decision-making in such an environment. Establishing a comprehensive, shared ethical framework for European pharmacists in e-pharmacies can foster a deeper understanding and appreciation of the opportunities afforded by emerging technologies. By facilitating the development of ethical conduct of pharmacists in e-pharmacies, such a framework can enhance patients’ well-being while promoting fundamental principles of autonomy, equity, and solidarity in this evolving field.

Much research is being carried out using publicly available Twitter data in the field of public health, but the types of research questions that these data are being used to answer and the extent to which these projects require ethical oversight are not clear. This review describes the current state of public health research using Twitter data in terms of methods and research questions, geographic focus, and ethical considerations including obtaining informed consent from Twitter handlers. We implemented a systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, of articles published between January 2006 and October 31, 2019, using Twitter data in secondary analyses for public health research, which were found using standardized search criteria on SocINDEX, PsycINFO, and PubMed. Studies were excluded when using Twitter for primary data collection, such as for study recruitment or as part of a dissemination intervention. We identified 367 articles that met eligibility criteria. Infectious disease (n=80, 22%) and substance use (n=66, 18%) were the most common topics for these studies, and sentiment mining (n=227, 62%), surveillance (n=224, 61%), and thematic exploration (n=217, 59%) were the most common methodologies employed. Approximately one-third of articles had a global or worldwide geographic focus; another one-third focused on the United States. The majority (n=222, 60%) of articles used a native Twitter application programming interface, and a significant amount of the remainder (n=102, 28%) used a third-party application programming interface. Only one-third (n=119, 32%) of studies sought ethical approval from an institutional review board, while 17% of them (n=62) included identifying information on Twitter users or tweets and 36% of them (n=131) attempted to anonymize identifiers. Most studies (n=272, 79%) included a discussion on the validity of the measures and reliability of coding (70% for interreliability of human coding and 70% for computer algorithm checks), but less attention was paid to the sampling frame, and what underlying population the sample represented. Twitter data may be useful in public health research, given its access to publicly available information. However, studies should exercise greater caution in considering the data sources, accession method, and external validity of the sampling frame. Further, an ethical framework is necessary to help guide future research in this area, especially when individual, identifiable Twitter users and tweets are shared and discussed. PROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=148170.

This paper pertains to research works aiming at linking ethics and automated reasoning in autonomous machines. It focuses on a formal approach that is intended to be the basis of an artificial agent’s reasoning that could be considered by a human observer as an ethical reasoning. The approach includes some formal tools to describe a situation and models of ethical principles that are designed to automatically compute a judgement on possible decisions that can be made in a given situation and explain why a given decision is ethically acceptable or not. It is illustrated on three ethical frameworks—utilitarian ethics, deontological ethics and the Doctrine of Double effect whose formal models are tested on ethical dilemmas so as to examine how they respond to those dilemmas and to highlight the issues at stake when a formal approach to ethical concepts is considered. The whole approach is instantiated on the drone dilemma, a thought experiment we have designed; this allows the discrepancies that exist between the judgements of the various ethical frameworks to be shown. The final discussion allows us to highlight the different sources of subjectivity of the approach, despite the fact that concepts are expressed in a more rigorous way than in natural language: indeed, the formal approach enables subjectivity to be identified and located more precisely.

In the rapidly evolving field of remote sensing, Deep Learning (DL) techniques have become pivotal in interpreting and processing complex datasets. However, the increasing reliance on these algorithms necessitates a robust ethical framework to evaluate their trustworthiness. This paper introduces a comprehensive ethical framework designed to assess and quantify the trustworthiness of DL techniques in the context of remote sensing. We first define trustworthiness in DL as a multidimensional construct encompassing accuracy, reliability, transparency and explainability, fairness, and accountability. Our framework then operationalizes these dimensions through a set of quantifiable metrics, allowing for the systematic evaluation of DL models. To illustrate the applicability of our framework, we selected an existing case study in remote sensing, wherein we apply our ethical assessment to a DL model used for classification. Our results demonstrate the model’s performance across different trustworthiness metrics, highlighting areas for ethical improvement. This paper not only contributes a novel framework for ethical analysis in the field of DL, but also provides a practical tool for developers and practitioners in remote sensing to ensure the responsible deployment of DL technologies. Through a dual approach that combines top-down international standards with bottom-up, context-specific considerations, our framework serves as a practical tool for ensuring responsible AI applications in remote sensing. Its application through a case study highlights its potential to influence policy-making and guide ethical AI development in this domain.

This paper presents a framework for ethical and responsible standardisation by adapting Responsible Research and Innovation (RRI) principles to standard-setting. While standardisation is sometimes viewed as technical and neutral, it has profound social and political effects. We identify relevant potential shortcomings in standardisation: overlooked social impacts, short-run market capture, and loss of expertise during certification. Further, we emphasise the distinct context of standardisation: its commercial roots, and the distinct way it serves as a sphere of knowledge-creation. Taking these points together, we land on a four-part framework: Inclusivity, Anticipation, Stewardship, and Leadership. The paper reviews institutional measures to embed the framework in committee processes.

Digital health has revolutionized the landscape of healthcare through personalized care, moving away from the traditional approach of treating symptoms and conditions. Digital devices provide diagnostic accuracy and treatment effectiveness while equipping patients with control over their health and well-being. Although the growth of technology provides unprecedented opportunities, there are also certain issues arising from the use of such technology. This scoping review aimed to explore perceived gaps and challenges in the use of digital technology by patients and meta-synthesize them. Identifying such gaps and challenges will encourage new insights and understanding, leading to evidence-informed policies and practices. Three electronic databases were searched (Cinahl EBSCO, Pubmed, and Web of Science) for papers published in English between January 2010 and December 2023. A narrative meta-synthesis was performed. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2009 checklist. A total of 345 papers were retrieved and screened, with a noticeable increase in publication numbers after 2015. After the final selection, a total of 28 papers were included in the final meta-synthesis; these were published between 2015 and 2023. A total of 99 individual reports were included in the synthesis of these papers, comprising 25 identified gaps and 74 challenges. Our meta-synthesis revealed several gaps and challenges related to patients' use of digital technology in health, including generational differences in digital propensity and deficiencies in the work process. In terms of ethics, the lack of trust in technology and data ownership was highlighted, with the meta-synthesis identifying issues in the realm of disruption of human rights. We, therefore, propose building a model for ethically aligned technology development and acceptance that considers human rights a crucial parameter in the digital healthcare ecosystem.