In recent years, research in robotics and artificial intelligence (AI) has made rapid progress. It is expected that robots and AI will play a part in the field of nursing and their role might broaden in the future. However, there are areas of nursing practice that cannot or should not be entrusted to robots and AI, because nursing is a highly humane practice, and therefore, there would, perhaps, be some practices that should not be replicated by robots or AI. Therefore, (...) this paper focuses on several ethical concepts (advocacy, accountability, cooperation, and caring) that are considered important in nursing practice, and examines whether it is possible to implement these ethical concepts in robots and AI by analyzing the concepts and the current state of robotics and AI technology. Advocacy: Among the components of advocacy, safeguarding and apprising can be more easily implemented, while elements that require emotional communication with patients, such as valuing and mediating, are difficult to implement. Accountability: Robotic nurses with explainable AI have a certain level of accountability. However, the concept of explanation has problems of infinite regression and attribution of responsibility. Cooperation: If robot nurses are recognized as members of a community, they require the same cooperation as human nurses. Caring: More difficulties are expected in care-receiving than in caregiving. However, the concept of caring itself is ambiguous and should be explored further. Accordingly, our analysis suggests that, although some difficulties can be expected in each of these concepts, it cannot be said that it is impossible to implement them in robots and AI. However, even if it were possible to implement these functions in the future, further study is needed to determine whether such robots or AI should be used for nursing care. In such discussions, it will be necessary to involve not only ethicists and nurses but also an array of society members. (shrink)

BackgroundWhether and how to disclose genomic findings obtained in the course of genomic clinical practice and medical research has been a controversial global bioethical issue over the past two decades. Although several recommendations and judgment tools for the disclosure of genomic findings have been proposed, none are sufficiently systematic or inclusive or even consistent with each other. In order to approach the disclosure/non-disclosure practice in an ethical manner, optimal and easy-to-use tools for supporting the judgment of physicians/researchers in genomic medicine (...) are necessary.MethodsThe bioethics literature on this topic was analyzed to parse and deconstruct the somewhat overlapping and therefore ill-defined key concepts of genomic findings, such as incidental, primary, secondary, and other findings. Based on the deconstruction and conceptual analyses of these findings, we then defined key parameters from which to identify the strength of duty to disclose (SDD) for a genomic finding. These analyses were then applied to develop a framework with the SDD matrix and systematic decision-making pathways for the disclosure of genomic findings.ResultsThe following six major parameters (axes), along with sub-axes, were identified: Axis 1 (settings and institutions where findings emerge); Axis 2 (presence or absence of intention and anticipatability in discovery); Axis 3 (maximal actionability at the time of discovery); Axis 4 (net medical importance); Axis 5 (expertise of treating physician/researcher); and Axis 6 (preferences of individual patients/research subjects for disclosure). For Axes 1 to 4, a colored SDD matrix for genomic findings was developed in which levels of obligation for disclosing a finding can be categorized. For Axes 5 and 6, systematic decision-making pathways were developed via the SDD matrix.ConclusionWe analyzed the SDD of genomic findings and developed subsequent systematic decision-making pathways of whether and how to disclose genomic findings to patients/research subjects and their relatives in an ethical manner. Our comprehensive framework may help physicians and researchers in genomic medicine make consistent ethical judgments regarding the disclosure of genomic findings. (shrink)

This paper presents a new algorithm to find an appropriate similarityunder which we apply legal rules analogically. Since there may exist a lotof similarities between the premises of rule and a case in inquiry, we haveto select an appropriate similarity that is relevant to both thelegal rule and a top goal of our legal reasoning. For this purpose, a newcriterion to distinguish the appropriate similarities from the others isproposed and tested. The criterion is based on Goal-DependentAbstraction (GDA) to select a (...) similarity such that an abstraction basedon the similarity never loses the necessary information to prove the ground (purpose of legislation) of the legal rule. In order to cope withour huge space of similarities, our GDA algorithm uses some constraintsto prune useless similarities. (shrink)

In their comprehensive review article, De Bie et al., using some references, discreetly point out that pregnant women’s decision in Domain III to undergo fetal extraction via C-section should conti...

Interventions performed on a pregnant woman's body can affect the fetus in multiple ways. Such effects can be harmful to beneficial to the fetus. Unfortunately, the effects of new drugs and compoun...

This is a review article of Tokuyasu Kakuta, Makoto Haraguchi, and Yoshiaki Okubo, "A Goal-Dependent Abstraction for Legal Reasoning by Analogy," /Artificial Intelligence and Law/ 5(March 1997): 97-118.