Marie-Christine Fritzsche

Clinical Ethics Consultation addresses ethical questions, conflicts, and dilemmas in healthcare. This article argues that there are ethical dimensions in Clinical Ethics Consultation that are difficult to identify and need to be addressed additionally in Clinical Ethics Consultation models. Despite the diverse models and instruments developed across different regions, existing approaches often do not explicitly address these dimensions. The article illustrates the following critical hidden dimensions: issues of power and knowledge, pointing to structural and epistemic injustices, as well as further epistemic issues of implicit biases and subjectivity, intersectional discrimination, and limited participation of patients and their relatives. We argue that improving practice requires greater awareness of these hidden dimensions, consultant self-reflexivity, epistemic humility, and the implementation of specific strategies to mitigate power imbalances, thereby better fulfilling the goal of Clinical Ethics Consultation to support nuanced ethical decision-making.

Integrating artificial intelligence (AI) into critical domains such as healthcare holds immense promise. Nevertheless, significant challenges must be addressed to avoid harm, promote the well-being of individuals and societies, and ensure ethically sound and socially just technology development. Innovative approaches like Embedded Ethics, which refers to integrating ethics and social science into technology development based on interdisciplinary collaboration, are emerging to address issues of bias, transparency, misrepresentation, and more. This paper aims to develop this approach further to enable future projects to effectively deploy it. Based on the practical experience of using ethics and social science methodology in interdisciplinary AI-related healthcare consortia, this paper presents several methods that have proven helpful for embedding ethical and social science analysis and inquiry. They include (1) stakeholder analyses, (2) literature reviews, (3) ethnographic approaches, (4) peer-to-peer interviews, (5) focus groups, (6) interviews with affected groups and external stakeholders, (7) bias analyses, (8) workshops, and (9) interdisciplinary results dissemination. We believe that applying Embedded Ethics offers a pathway to stimulate reflexivity, proactively anticipate social and ethical concerns, and foster interdisciplinary inquiry into such concerns at every stage of technology development. This approach can help shape responsible, inclusive, and ethically aware technology innovation in healthcare and beyond.

Background Over 300 million individuals worldwide live with Atopic Dermatitis and Psoriasis, which are among the most common chronic inflammatory skin diseases. Multimodal biomarkers are currently being developed using large-scale data and artificial intelligence to allow for more targeted prediction and to improve treatment of patients with Atopic Dermatitis/Psoriasis. Although this promises enormous benefits for patients, it comes with critical challenges. This article offers an in-depth analysis of the ethical challenges in research and application of data-driven biomarkers in chronic inflammatory skin disease, which, as recent work shows, has not yet been explored in depth. Methods We conducted an interview study with 28 members of the BIOMarkers in Atopic Dermatitis and Psoriasis consortium including multiple stakeholder groups involved in biomarker research and application following the COREQ checklist. The interviews were analysed and interpreted theme-oriented using an updated grounded theory approach. Results The interviews revealed interconnected ethical challenges described by a wide range of stakeholders involved in biomarker research. Our analysis identified two broad categories of ethical challenges – disease-related and biomarker-related issues – from which three cross-cutting themes emerged: multiple forms of harm, multiple injustices, and multiple uncertainties. Disease-related challenges include covert psycho-socio-physical dimensions of harm and suffering in Atopic Dermatitis/Psoriasis, quality of life impacts, trial-and-error approaches, and communication and expectation management in clinical practice. Biomarker-related challenges range from big data use with multiple biases in the different data-sets, stratification of patients into subgroups, to invasiveness of diagnostic measures, multiple uncertainties and expectation management in science. This article also provides stakeholder suggestions for mitigating harm associated with Atopic Dermatitis/Psoriasis and biomarker use to inform policy development. Conclusions The identification of the many ethical challenges uncovered in the interviews and the nuanced view of harm, intersecting injustices including epistemic injustice, and the multiple uncertainties provide crucial considerations for evaluating the risks and benefits of biomarker research and application in healthcare. These insights should inform policy development for data/AI-driven biomarker use for Atopic Dermatitis/Psoriasis and support research practice, public health interventions, and clinical practice to develop and apply medical innovations that are ethically responsible.