More Process, Less Principles: The Ethics of Deploying AI and Robotics in Medicine.

Current national and international guidelines for the ethical design and development of artificial intelligence (AI) and robotics emphasize ethical theory. Various governing and advisory bodies have generated sets of broad ethical principles, which institutional decisionmakers are encouraged to apply to particular practical decisions. Although much of this literature examines the ethics of designing and developing AI and robotics, medical institutions typically must make purchase and deployment decisions about technologies that have already been designed and developed. The primary problem facing medical institutions is not one of ethical design but of ethical deployment. The purpose of this paper is to develop a practical model by which medical institutions may make ethical deployment decisions about ready-made advanced technologies. Our slogan is "more process, less principles." Ethically sound decisionmaking requires that the process by which medical institutions make such decisions include participatory, deliberative, and conservative elements. We argue that our model preserves the strengths of existing frameworks, avoids their shortcomings, and delivers its own moral, practical, and epistemic advantages.

Beneficent dehumanization: Employing artificial intelligence and carebots to mitigate shame-induced barriers to medical care.

As costs decline and technology inevitably improves, current trends suggest that artificial intelligence (AI) and a variety of "carebots" will increasingly be adopted in medical care. Medical ethicists have long expressed concerns that such technologies remove the human element from medicine, resulting in dehumanization and depersonalized care. However, we argue that where shame presents a barrier to medical care, it is sometimes ethically permissible and even desirable to deploy AI/carebots because (i) dehumanization in medicine is not always morally wrong, and (ii) dehumanization can sometimes better promote and protect important medical values. Shame is often a consequence of the human-to-human element of medical care and can prevent patients from seeking treatment and from disclosing important information to their healthcare provider. Conditions and treatments that are shame-inducing offer opportunities for introducing AI/carebots in a manner that removes the human element of medicine but does so ethically. We outline numerous examples of shame-inducing interactions and how they are overcome by implementing existing and expected developments of AI/carebot technology that remove the human element from care.

Artificial Intelligence and Robotics in Nursing: Ethics of Caring as a Guide to Dividing Tasks Between AI and Humans.

Nurses have traditionally been regarded as clinicians that deliver compassionate, safe, and empathetic health care (Nurses again outpace other professions for honesty & ethics, 2018). Caring is a fundamental characteristic, expectation, and moral obligation of the nursing and caregiving professions (Nursing: Scope and standards of practice, American Nurses Association, Silver Spring, MD, 2015). Along with caring, nurses are expected to undertake ever-expanding duties and complex tasks. In part because of the growing physical, intellectual and emotional demandingness, of nursing as well as technological advances, artificial intelligence (AI) and AI care robots are rapidly changing the healthcare landscape. As technology becomes more advanced, efficient, and economical, opportunities and pressure to introduce AI into nursing care will only increase. In the first part of the article, we review recent and existing applications of AI in nursing and speculate on future use. Second, situate our project within the recent literature on the ethics of nursing and AI. Third, we explore three dominant theories of caring and the two paradigmatic expressions of caring (touch and presence) and conclude that AI-at least for the foreseeable future-is incapable of caring in the sense central to nursing and caregiving ethics. We conclude that for AI to be implemented ethically, it cannot transgress the core values of nursing, usurp aspects of caring that can only meaningfully be carried out by human beings, and it must support, open, or improve opportunities for nurses to provide the uniquely human aspects of care.

Difficult Discharge in the Context of Suspected Malingering: Reflections on the Value of Epistemic and Professional Independence.

During a clinical ethics fellow's first week of independent supervised service, two unhoused patients on the same floor were resisting the medical team's recommendations to discharge. In the team's view, both were medically stable and no longer required hospitalization in an acute setting. The medical team suspected malingering for both. The social worker and case manager had employed their usual means of gentle persuasion and eliminating psychosocial barriers to no avail. Rather than call the police, the attending physician, social worker, and case manager decided to call ethics. These cases lead the fledgling fellow to consider the appropriate role for ethicists in difficult discharge cases. The article analyzes each case, evaluates their similarities and differences in the context of suspected malingering, and comments on ethical issues surrounding cases of suspected malingering. Finally, the authors reflect on the value and importance of developing and maintaining epistemic and professional independence.

Evolving Landscape of Ethics in Oncology: A Journey Through the Past, Present, and Future.

Providing a brief overview of past, present, and future ethics issues in oncology, this article begins with historical contexts, including the paternalistic approach to cancer care. It delves into present-day challenges such as navigating cancer treatment during pregnancy and addressing health care disparities faced by LGBTQ+ individuals. It also explores the ethical implications of emerging technologies, notably artificial intelligence and Big Data, in clinical decision making and medical education.