BackgroundHealthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use.MethodsPubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was title and abstract (...) screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis.ResultsArtificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process.ConclusionsThe prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human–machine interaction, have been neglected in the debate on AI for clinical ethics so far.Trial registrationThis review is registered at Open Science Framework (https://osf.io/wvcs9). (shrink)

In this paper, we suggest that personalized LLMs trained on information written by or otherwise pertaining to an individual could serve as artificial moral advisors (AMAs) that account for the dynamic nature of personal morality. These LLM-based AMAs would harness users’ past and present data to infer and make explicit their sometimes-shifting values and preferences, thereby fostering self-knowledge. Further, these systems may also assist in processes of self-creation, by helping users reflect on the kind of person they want to be (...) and the actions and goals necessary for so becoming. The feasibility of LLMs providing such personalized moral insights remains uncertain pending further technical development. Nevertheless, we argue that this approach addresses limitations in existing AMA proposals reliant on either predetermined values or introspective self-knowledge. (shrink)

This issue of The Journal of Medicine and Philosophy is dedicated to topics in clinical ethics with essays addressing clinician participation in state sponsored execution, duties to decrease ecological footprints in medicine, the concept of caring and its relationship to conscientious refusal, the dilemmas involved in dual use research, a philosophical and practical critique of principlism, conundrums that arise when applying surrogate decision-making models to patients with moderate intellectual disabilities, the phenomenology of chronic disease, and ethical concerns surrounding the use (...) of artificial intelligence in medicine. Throughout the issue, the themes of conceptual and moral boundaries in bioethics, critiques of current clinical ethics paradigms, and the importance of transparency are prominent. (shrink)

Earp et al. (2024) sketch a thought-provoking potential use of generative AI to enhance supported decision-making for adults who have lost capacity/competence to make their own medical decisions. T...