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There are intense discussions about the ethical and societal implications of biomedical engineering, but little data to suggest how scientists think about the ethics of their work. The aim of this study is to describe how scientists frame the ethics of their research, with a focus on the field of molecular systems engineering. Semi-structured qualitative interviews were conducted during 2021–2022, as part of a larger study. This analysis includes a broad question about how participants view ethics as related to their work, with follow up probes about the topics they consider most important. Interviews were transcribed, inductively coded by two researchers to consensus, and analyzed thematically. Twenty-four scientists participated in the study. Interviewees hold positions as professors, principal investigators, and senior staff researchers in universities or research institutes in the United States and Europe. Among those scientists who reported reflecting on ethical considerations in their work, many equated ethics with research ethics topics (e.g., safety, replicability), or with regulation and guidelines. Participants expressed the view that ethical issues are primarily relevant for clinical trials of bioengineered products, or for those working with animal or human subjects. Scientists described their research as "too early" or "not examining anything living" with regard to ethical reflection. Finally, many felt that ethics is seen as territory for experts and therefore beyond scientists' competencies. Molecular systems engineering scientists currently focus on regulatory aspects as the framework for their ethical analyses. They describe using a framework to define when life arises, as a means to determine when further ethical engagement is warranted. Further research is needed to investigate how scientists relate to the ethics of their scientific work, and build consensus around concepts of life, autonomous behavior, and physiological relevance of bioengineered systems. [ABSTRACT FROM AUTHOR]

Digital health innovation is expected to transform healthcare, but it also generates ethical and societal concerns, such as privacy risks, and biases that can compound existing health inequalities. While such concerns are widely recognized, existing regulatory principles, oversight methods and ethical frameworks seem out of sync with digital health innovation. New governance and innovation best practices are thus needed to bring such principles to bear with the reality of business, innovation, and regulation. To grant practical insight into best practices for responsible digital health innovation, we conducted a qualitative study based on an interactive engagement methodology. We engaged key stakeholders (n = 46) operating at the translational frontier of digital health. This approach allowed us to identify three clusters of governance and innovation best practices in digital health innovation: i) inclusive co-creation, ii) responsive regulation, and iii) value-driven innovation. Our study shows that realizing responsible digital health requires diverse stakeholders' commitment to adapt innovation and regulation practices, embracing co-creation as the default modus operandi for digital health development. We describe these collaborative practices and show how they can ensure that innovation is neither slowed by overregulation, nor leads to unethical outcomes. [ABSTRACT FROM AUTHOR]

Data access and data sharing are vital to advance medicine. A growing number of public private partnerships are set up to facilitate data access and sharing, as private and public actors possess highly complementary health data sets and treatment development resources. However, the priorities and incentives of public and private organizations are frequently in conflict. This has complicated partnerships and sparked public concerns around ethical issues such as trust, justice or privacy—in turn raising an important problem in business and data ethics: how can ethical theory inform the practice of public and private partners to mitigate misaligned incentives, and ensure that they can deliver societally beneficial innovation? In this paper, we report on the development of the Swiss Personalized Health Network's ethical guidelines for health data sharing in public private partnerships. We describe the process of identifying ethical issues and engaging core stakeholders to incorporate their practical reality on these issues. Our report highlights core ethical issues in health data public private partnerships and provides strategies for how to overcome these in the Swiss health data context. By agreeing on and formalizing ethical principles and practices at the beginning of a partnership, partners and society can benefit from a relationship built around a mutual commitment to ethical principles. We present this summary in the hope that it will contribute to the global data sharing dialogue. [ABSTRACT FROM AUTHOR]

Over the last decade, bioengineering has seen a sustained growth in scientific publications, patents, and clinical trials. As the field attempts to bridge the gap between discovery and clinical application, a broader societal dialogue is needed to build public trust and address potential ethical, societal, and regulatory challenges. In this essay, we discuss societal aspects linked to the clinical use of biomedical engineering approaches and technologies, with a specific focus on molecular systems engineering. Drawing on data from interviews with 24 scientists, we identified four key aspects for fostering societal support for translational efforts in this domain: (1) effective science communication and internal awareness; (2) open societal dialogue; (3) fair and equitable access to new technologies; and (4) adequate science and technology policies. We conclude that molecular systems engineering would benefit from anticipating future challenges with the view of building a robust bond of trust with lay publics, regulators, and society at large. [ABSTRACT FROM AUTHOR]

Drugs addressing unmet medical needs can change the lives of millions. Developing and validating new drugs can, however, take many years. To streamline the assessment of new drugs, regulatory agencies have long established shortened review pathways. Among these programs, Accelerated Approval (AA) has recently come under scrutiny due to the U.S. Food and Drug Administration's decision to authorize Aducanumab, the first Alzheimer's disease drug. This decision attracted fierce criticism due to the allegedly insufficient evidence about the safety and efficacy of the drug. While considerable scholarly attention has been devoted to this case, the ethical aspects of the AA regulatory pathway have so far remained relatively unexplored. In this paper, we set out to fill this gap. We illustrate six conditions that should be met for AA to be ethically acceptable: moral solicitude, evidence, risk mitigation, impartiality, sustainability, and transparency. We discuss such conditions and suggest practical steps to implement them in regulatory and oversight processes. Taken together, our six conditions represent a benchmark for assessing the ethical validity of AA processes and decisions. [ABSTRACT FROM AUTHOR]

A new active substance called "dersimelagon" (MT-7117) is being tested as an alternative treatment option for Erythropoietic protoporphyria (EPP). At the moment, dersimelagon is being tested both in the US and in Europe in a phase III placebo-controlled RCT. However, given the availability of an already approved treatment option for EPP the use of a placebo arm is questionable from an ethics point of view. We analyze the issue and suggest that a noninferiority active-control trial without placebo is an ethically and scientifically more valid design to test the efficacy of dersimelagon as well as other EPP treatments. [ABSTRACT FROM AUTHOR]

Introduction: Artificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals' health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors', family members', and healthcare professionals' expectations and attitudes towards medical AI. Methods: We followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals' expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis. Results: Based on the participants' deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients' rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship. Conclusion: Our findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care. [ABSTRACT FROM AUTHOR]

This paper describes the results of a multi-country survey of governance approaches for the use of digital contact tracing (DCT) in response to the COVID-19 pandemic. We argue that the countries in our survey represent two distinct models of DCT governance, both of which are flawed. The "data protection model" emphasizes privacy protections at the expense of public health benefit, while the "emergency response model" sacrifices transparency and accountability, prompting concerns about excessive governance surveillance. The ethical and effective use of DCT in the future requires a new governance approach that is better suited to this novel use of mobile phone data to promote public health." [ABSTRACT FROM AUTHOR]

Background: Ethics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts.Main Text: In this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC's scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC's way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science.Conclusions: We believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large. [ABSTRACT FROM AUTHOR]

The article presents the discussion on Machine learning (ML) having highly transformative approaches to the automation of numerous clinical tasks. Topics include computer scientists being most concerned with demonstrating the superiority of ML-driven systems at performing classificatory or predictive tasks; and focusing on ensuring the safety and effectiveness of medical devices, software, and drugs before receiving market authorization.

In 2017 the Swiss federal government established the Swiss Personalized Health Network (SPHN), a nationally coordinated data infrastructure for genetic research. The SPHN advisory group on Ethical, Legal, and Social Implications (ELSI) was tasked with the creation of a recommendation to ensure ethically responsible reporting of genetic research findings to research participants in SPHN-funded studies. Following consultations with expert stakeholders, including geneticists, pediatricians, sociologists, university hospitals directors, patient representatives, consumer protection associations, and insurers, the ELSI advisory group issued its recommendation on "Reporting actionable genetic findings to research participants" in May 2020. In this paper we outline the development of this recommendation and the provisions it contains. In particular, we discuss some of its key features, namely: (1) that participation in SPHN-funded studies as a research subject is conditional to accepting that medically relevant genetic research findings will be reported; (2) that a Multidisciplinary Expert Panel (MEP) should be created to support researchers' decision-making processes about reporting individual genetic research findings; (3) that such Multidisciplinary Expert Panel will make case-by-case decisions about whether to allow reporting of genetic findings, instead of relying on a pre-defined list of medically relevant variants; (4) that research participants shall be informed of the need to disclose genetic mutations when applying for private insurance, which may influence individual decisions about participation in research. By providing an account of the procedural background and considerations leading to the SPHN recommendation on "Reporting actionable genetic findings to research participants," we seek to promote a better understanding of the proposed guidance, as well as to contribute to the global dialog on the reporting of genetic research findings. [ABSTRACT FROM AUTHOR]

With the birth of genetically engineered twins in November 2018, international debate about human genome editing governance has moved from an emphasis on mutual engagement among multiple stakeholders to a self-regulatory model enacted through high-level expert groups with little or no public input. This article reconstructs this paradigm shift and suggests that inclusive public deliberation should still have a role in public decision making about genome editing. [ABSTRACT FROM AUTHOR]

In this article, the author reports Ethics, Values, and Responsibility in Human Genome Editing. Topics include CRISPR/Cas9 genome editing, an inexpensive and efficient tool to introduce changes in Deoxyribonucleic acid(DNA); include correcting congenital monogenic disorders, targeting disease-causing molecular lesions, and altering multiple genetic loci at the same time; and used to enhance human traits,3 such as resistance to infectious diseases, strength, or cognitive capacity.

Effy Vayena and colleagues argue that machine learning in medicine must offer data protection, algorithmic transparency, and accountability to earn the trust of patients and clinicians. [ABSTRACT FROM AUTHOR]

Effy Vayena and colleagues argue that machine learning in medicine must offer data protection, algorithmic transparency, and accountability to earn the trust of patients and clinicians. [ABSTRACT FROM AUTHOR]

This study reports on 13 semistructured in-depth interviews to qualitatively explore the experiences of individuals who publicly shared their direct-to-consumer genetic testing results on the platform openSNP. In particular, we focused on interviewees' understanding of privacy. Participants reported that the likelihood and the magnitude of privacy harms depend on gender, ethnicity, sexual orientation, the stigma associated with certain clinical conditions, the existence of adequate legislation, and the nature of national health care systems. Some participants expressed the view that those who enjoy higher socioeconomic status or are better protected by their country's legislation have a responsibility to share their genetic data. Our study shows that people who share their genetic data publicly online—far from being insensitive to privacy risks—have a complex understanding of the social, relational, and contextual nature of genetic privacy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) systems, especially those employing machine learning methods, are often considered black boxes, that is, systems whose inner workings and decisional logics remain fundamentally opaque to human understanding. In this article, we set out to clarify what the new General Data Protection Regulation (GDPR) says on profiling and automated decision-making employing opaque systems. More specifically, we focus on the application of such systems in the domain of healthcare. We conducted a conceptual analysis of the notion of opacity (black box) using concrete examples of existing or envisaged medical applications. Our analysis distinguishes among three forms of opacity: (i) lack of disclosure, (ii) epistemic opacity, and (iii) explanatory opacity. For each type of opacity, we discuss where it originates from, and how it can be dealt with according to the GDPR in the context of healthcare. This analysis can offer insights regarding the contested issue of the explainability of AI systems in medicine, and its potential effects on the patient-doctor relationship. [ABSTRACT FROM AUTHOR]

Massive amounts of data are collected and stored on a routine basis in virtually all domains of human activities. Such data are potentially useful to biomedicine. Yet, access to data for research purposes is hindered by the fact that different kinds of individual-patient data reside in disparate, unlinked silos. We propose that data cooperatives can promote much needed data aggregation and consequently accelerate research and its clinical translation. Data cooperatives enable direct control over personal data, as well as more democratic governance of data pools. This model can realize a specific kind of data economy whereby citizens and communities are empowered to steer data use according to their motivations, preferences, and concerns. Policy makers can promote this model by recognizing citizens’ rights to access and to obtain a copy of their own data, and by funding distributed data infrastructures piloting new data aggregation models. [ABSTRACT FROM AUTHOR]

To address the ethical challenges in big data health research we propose the concept of systemic oversight. This approach is based on six defining features (adaptivity, flexibility, monitoring, responsiveness, reflexivity, and inclusiveness) and aims at creating a common ground across the oversight pipeline of biomedical big data research. Current trends towards enhancing granularity of informed consent and specifying legal provisions to address informational privacy and discrimination concerns in data-driven health research are laudable. However, these solutions alone cannot have the desired impact unless oversight activities by different stakeholders acquire a common substantive orientation. [ABSTRACT FROM AUTHOR]

Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it. [ABSTRACT FROM AUTHOR]

We explored the characteristics and motivations of people who, having obtained their genetic or genomic data from Direct-To-Consumer genetic testing (DTC-GT) companies, voluntarily decide to share them on the publicly accessible web platform openSNP. The study is the first attempt to describe open data sharing activities undertaken by individuals without institutional oversight. In the paper we provide a detailed overview of the distribution of the demographic characteristics and motivations of people engaged in genetic or genomic open data sharing. The geographical distribution of the respondents showed the USA as dominant. There was no significant gender divide, the age distribution was broad, educational background varied and respondents with and without children were equally represented. Health, even though prominent, was not the respondents’ primary or only motivation to be tested. As to their motivations to openly share their data, 86.05% indicated wanting to learn about themselves as relevant, followed by contributing to the advancement of medical research (80.30%), improving the predictability of genetic testing (76.02%) and considering it fun to explore genotype and phenotype data (75.51%). Whereas most respondents were well aware of the privacy risks of their involvement in open genetic data sharing and considered the possibility of direct, personal repercussions troubling, they estimated the risk of this happening to be negligible. Our findings highlight the diversity of DTC-GT consumers who decide to openly share their data. Instead of focusing exclusively on health-related aspects of genetic testing and data sharing, our study emphasizes the importance of taking into account benefits and risks that stretch beyond the health spectrum. Our results thus lend further support to the call for a broader and multi-faceted conceptualization of genomic utility. [ABSTRACT FROM AUTHOR]

With the launch of the Precision Medicine Initiative (PMI) in January 2015, the White House granted a high degree of federal support to an emerging biomedical paradigm. What explains this level of political recognition? Based on literature and policy analysis, we reconstruct the scientific and the legislative paths that led to the political endorsement of precision medicine. First, we describe the proliferation of personalized approaches to therapy ignited by the discovery of hemoprotein cytochrome P450 polymorphism in 1988. Then, we analyze the legislative history of precision medicine, from the unsuccessful introduction of Genomics and Personalized Medicine Acts in the second half of the last decade, to the highly acclaimed PMI. However, scientific progress and political contingency alone do not explain the upheaval of precision medicine as an institutionally supported initiative. On examination, the launch of a Precision Medicine Research Cohort and the incorporation of a participatory ethos into the fabric of the PMI proved to be crucial determinants of the political support for precision medicine. Weaving together the scientific and legislative antecedents of precision medicine, we illuminate how the mutual constitution of science and social order generates political recognition for innovative biomedical paradigms. [ABSTRACT FROM AUTHOR]

Physical frailty and loss of muscular mass (sarcopenia) are believed to be predictors of age-related conditions, such as disability and loss of autonomy. In this paper, I show that what in political and moral philosophy has come to be known as 'the capability approach' may indeed provide much needed conceptual clarification in the area of frailty research. Other than being useful at the theoretical level, the capability approach can definitely help in the implementation of clinical guidelines and public health measures aimed at translating the results that are accumulating from frailty research. I will first briefly review the main philosophical tenets of the capability approach, and then analyze how they relate to current debates about frailty and sarcopenia by introducing the notion of 'enablement'. Finally, I will show how my analysis bears on both clinical research in this domain and public policy aimed at tackling some of the age-related issues in an aging society. [ABSTRACT FROM AUTHOR]

Precision medicine promises to develop diagnoses and treatments that take individual variability into account. According to most specialists, turning this promise into reality will require adapting the established framework of clinical research ethics, and paying more attention to participants' attitudes towards sharing genotypic, phenotypic, lifestyle data and health records, and ultimately to their desire to be engaged as active partners in medical research. Notions such as participation, engagement and partnership have been introduced in bioethics debates concerning genetics and large-scale biobanking to broaden the focus of discussion beyond individual choice and individuals' moral interests. The uptake of those concepts in precision medicine is to be welcomed. However, as data and medical information from research participants in precision medicine cohorts will be collected on an individual basis, translating a participatory approach in this emerging area may prove cumbersome. Therefore, drawing on Joseph Raz's perfectionism, we propose a principle of respect for autonomous agents that, we reckon, can address many of the concerns driving recent scholarship on partnership and public participation, while avoiding some of the limitations these concept have in the context of precision medicine. Our approach offers a normative clarification to how becoming partners in precision is compatible with retaining autonomy. Realigning the value of autonomy with ideals of direct engagement, we show, can provide adequate normative orientation to precision medicine; it can do justice to the idea of moral pluralism by stressing the value of moral self-determination: and, finally, it can reconcile the notion of autonomy with other more communitarian values such as participation and solidarity. [ABSTRACT FROM AUTHOR]

The article focuses on the challenges in finding a suitable expression in French to refer to CRISPR-Cas9, referencing the article ""Editing" genes: A case study about how language matters in bioethics" by M. O'Keefe and colleagues within the issue. Topics mentioned include how adopting the English wording would oppose a national language being a civic asset, the principle of dialogic responsibility, and the use of the expression targeted genome engineering to refer to CRISPR-Cas9 in general.

The article talks about informed consent of the participants in medical research and the disclosure of clinical results to the participants. Topics discussed include the field of genomic research, the patients undergoing genome sequencing and seeking information on the incidental findings, and the concept of the disclosure of results being a right of the patients.

Transcription factor (TF)--induced reprogramming of somatic cells into induced pluripotent stem cells (iPSC) is associated with genome-wide changes in chromatin modifications. Polycomb-mediated histone H3 lysine-27 trimethylation (H3K27me3) has been proposed as a defining mark that distinguishes the somatic from the iPSC epigenome. Here, we dissected the functional role of H3K27me3 in TF--induced reprogramming through the inactivation of the H3K27 methylase EZH2 at the onset of reprogramming. Our results demonstrate that surprisingly the establishment of functional iPSC proceeds despite global loss of H3K27me3. iPSC lacking EZH2 efficiently silenced the somatic transcriptome and differentiated into tissues derived from the three germ layers. Remarkably, the genome-wide analysis of H3K27me3 in Ezh2 mutant iPSC cells revealed the retention of this mark on a highly selected group of Polycomb targets enriched for developmental regulators controlling the expression of lineage specific genes. Erasure of H3K27me3 from these targets led to a striking impairment in TF--induced reprogramming. These results indicate that PRC2-mediated H3K27 trimethylation is required on a highly selective core of Polycomb targets whose repression enables TF--dependent cell reprogramming. [ABSTRACT FROM AUTHOR]

Since the first successful application of the gene editing method based on the CRISPR/Cas-system, the technology has demonstrated great potential but also sparked a series of ethical concerns. Some of the issues are already known from earlier gene editing debates. However, the possibility of CRISPR to target genes with high accuracy and the easy application that allows a biohacker to experiment with a simple toolkit ordered online have introduced new ethical challenges. Further, thanks to preprint servers such as bioRxiv, biomedical research results are more and more accessible with little delay after an experiment was conducted. This enables researchers all over the world to participate and conduct their own experiments, making it a global endeavour. Not only does this make it difficult to monitor and regulate the technology but also speeds up the technological development significantly.CRISPR is only one of many examples of recent advancements with potentially high consequences for society at large. We think it is therefore paramount to identify new issues, understand their nature and assess their impact in a timely manner. In this paper, we propose the integration of digital methods into the toolbox of modern empirical bioethics and demonstrate their potential with two examples: We used 1) crawling and network analysis for hypothesis building, and 2) sentiment analysis to assess the public's attitudes towards CRISPR on Twitter over a six and a half years period. [ABSTRACT FROM AUTHOR]

The authors discuss the report "Do researchers have an obligation to actively look for genetic incidental findings?" by C. Gliwa and B. E. Berkman from the volume 13, number 2013 issue, which looks at ethical considerations related to the disclosure of secondary research results. It is noted that not all researchers in a project posses the expertise to accurately interpret incidental findings. Particular focus is given to pediatric genomics and the disclosure of autosomal recessive disorders.

The authors discuss the article "The argument from potentiality in the embryo protection debate: Finally 'depotentialized'?" by M. S. Stier and B. Schoene-Seifert from the volume 13 number 1, 2013 issue, with particular focus on the ethics and regulation of human embryonic stem cell (hESC) research. They argue that scientific evidence on cellular reprogramming necessitates changes to hESC research policies based on the potentiality argument (PA) of the moral status of embryos.

The article focuses on the benefits and disadvantages that genetic research participants and the families of genetic research participants could find from having the results of advanced genetic testing disclosed to them. In the article the authors offer their opinions on the moral and issues that are raised with the disclosure of genetic testing results and on several genetic testing programs.

Background: Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice.Methods: Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the "Principles of Biomedical Ethics" by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI.Results: Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health.Conclusions: To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward. [ABSTRACT FROM AUTHOR]