According to the sufficiency theory of justice in health, justice requires that people have equal access to adequate health. In this article, I lay out the structure of this view and I assess its distributive implications for setting priority (i) between health needs across persons and (ii) between health care spending and other societal goods. I argue, first, that according to the sufficiency theory, deficiency in health cannot be completely offset by providing other societal goods. And, second, that it can prevent the medicalization of societies by stressing that improvements beyond the level of adequate health have relatively little weight, if any, from the standpoint of justice.

Background: The inclusion of neural networks in the work of healthcare institutions and medical education is an urgent problem in bioethics – a discipline that develops issues of personal choice between benefit and harm, between good and evil, between the volume and quality of information processing. The introduction of neural networks into the practice of healing is inevitable and is "the most commonly used analytical tool". The pros and cons of digitalization of medicine are described in detail in the literature, such as acquiring a digital assistant for diagnosis, determining optimal treatment plans and monitoring the health status of patients. Aim: to consid-er the possibility of improving clinical thinking in partnership with neural networks using the example of analyzing clinical situations.Materials and methods: An analytical review of the literature on the problem of integrating artificial intelligence into medical practice was carried out. The empirical base is represented by materials from qualitative sociological research (case study method). Results: Based on the analysis of cases, it is shown that the guidelines for writing out recipes for correcting errors are spelled out abstractly (the neural network is irresponsible, human intelligence must exceed the intelligence of the machine) and concretely (the initial answer of the neural network to the question posed is superficial and requires clarification using questions unexpected for the neural network spe-cific configuration of terms not recognized by artificial intelligence as keywords). The risks of introducing artificial intelligence into the work of medical institutions have been identified: on the one hand, with a high degree of compliance of doctors to the recommenda-tions of neural networks, the doctor is responsible for their errors, and the patient suffers, on the other hand, with a high degree of com-pliance of AI to user requests, training neural networks in dialogues is dangerous multiplication of dubious recommendations from un-differentiated/incompetent users. The doctor’s competence in dialogues training the neural network is invisible, unverified, and essen-tially virtual. Conclusion: Based on the conducted research, the possibility of improving neural networks through their adaptation to regional paradigms of healing, to value systems that are based on the archetypes of domestic healthcare is shown.

A serious challenge to the patient-oriented model of interaction between doctor and patient is posed by objective circum-stances: the constantly increasing number of patients and rising healthcare costs and, therefore, the inability to provide the attention and care necessary for the patient. A likely response to the emerging "crisis of care" in medicine appears to be the use of digital technolo-gies in clinical practice and healthcare organization. In particular, one of the possible options for using the potential of digital technolo-gies in medicine is artificial intelligence systems (hereinafter referred to as AI).The article is devoted to the peculiarities of communica-tion between a doctor and patients in the conditions of using artificial intelligence in clinical practice. The author identifies both the prospects for the introduction of artificial intelligence and difficulties in the process of communication between a doctor and a patient. The purpose of the article is to analyze the mechanism of influence of artificial intelligence on the process of communication between a doctor and a patient, to assess the impact of digital technologies on the basic values that develop in the process of clinical communica-tion – respect for patient autonomy, trust, confidentiality. The main expected positive consequence of the use of AI in medicine is the freeing up of time for direct communication with the patient. However, there is a risk that it will be used to increase patient flow, espe-cially in the commercial medical sector. It is concluded that, despite the widespread use of digital technologies, society remains wary of such innovations, especially when it concerns personal data. As a result, the level of trust and willingness to use AI systems is low. When introducing AI, it is necessary to take into account the feasibility and appropriateness of using these technologies in the provision of medical care, since, along with the effectiveness and accuracy of diagnostic results and treatment, the process of communication with a doctor remains extremely important for the patient, the opportunity to share their medical history and, during this communica-tion, form trust necessary for joint decision-making.

Background: The high level of competition in the educational services market in the Russian Federation is due to the large number of offers from public and private educational organizations of higher education. In accordance with current legislation, an applicant has the right to apply to several universities in the country at once and, as a rule, upon admission, gives preference to those whose brand is most famous. All this dictates the need to actively promote the brand of an educational organization using the capabili-ties of the mass media. At the same time, the budgets of regional educational organizations are often limited in the ability to conduct full-fledged advertising campaigns and do not provide funding for marketing activities to promote the university brand.Aim: to determine the impact of students’ original content on social networks on the processes of promoting the brand of an edu-cational organization. Materials and methods: An analysis of modern open access literary sources on the problem of forming the image and promoting the brand of an educational organization, as well as a content analysis of publications in social networks and the media, was carried out. The empirical basis of the study consisted of materials from a sociological study carried out using Googleforms, N = 520, average age (22.2 ± 2.37). The author's questionnaire underwent sociological and ethical examination; the norms of confidentiality and autonomy were observed in relation to the respondents. Mathematical data processing was carried out using variation statistics methods using the Excel for Windows Statistica 21.0 application package. Results: In just one month, students published 25 gaming videos on their personal pages for Russian Student Day. Each of them received from 2 to 20 thousand impressions during the competition. Students accompanied their publications with the hashtags speci-fied in the competition conditions, which made it possible to systematize the posted material, and when searching for information about the university on a social network, quickly find the necessary content. Conclusion: It has been shown that systematization in the collection, planning, and dissemination of information contributes to the rapid promotion of a positive image in the external and internal environment due to the displacement of negative information reasons from the agenda, provides the ability to manage information flows, and minimizes information risks.

Mobile health tools are often said to empower users by providing them with the information they need to exercise control over their health. We aim to bring clarity to this claim, and in doing so explore the relationship between empowerment and autonomy. We have identified three distinct models embedded in the empowerment rhetoric: empowerment as information, empowerment as control, and empowerment as values. Each distinct model of empowerment gives rise to an associated problem. These problems, the Problem of Interpretation, the Value Alignment Problem, and the Priority Problem, show that mobile health tools in their current form are either insufficient for empowerment or are self-defeating. These digital health technologies encourage users to adopt an individualized conception of autonomy, one that may weaken the doctor-patient relationship and undermine practices in shared decision making, and ultimately may not deliver on improving the health outcomes for those that need it the most.

In times of person-centered care, it is all the more important to support patients in making good decisions about their care. One way to offer such support to patients is by way of Patient Decision Aids (PDAs). Ranging from patient brochures to web-based tools, PDAs explicitly state the decisions patients face, inform them about their medical options, help them to clarify and discuss their values, and ultimately make a decision. However, lingering discussions surround effectiveness research on PDAs. In this article, I focus on two subjective measures of decision quality that are widely used as outcome measures in effectiveness research on PDAs (i.e., the Decisional Conflict Scale (DCS) and measures of regret). Although these measurement instruments have attracted critical attention in the scientific literature, bioethicists have hardly engaged with them. Therefore, I set myself to analyze the relationship between (the different subscales of) the DCS and measures of regret, on the one hand, and ethical principles such as beneficence and autonomy, on the other hand. In light of that analysis, I will clarify some discussions regarding the use of these measures of decision quality in effectiveness research on PDAs. This should help us to align the way we evaluate PDAs with ethical principles and avoid that our attempts to support patients in making good decisions about their care that is so central to person-centered care point in unethical directions.

Antimicrobial resistance (AMR) is an urgent, global threat to public health. The development and implementation of effective measures to address AMR is vitally important but presents important ethical questions. This is a policy area requiring further sustained attention to ensure that policies proposed in National Action Plans (NAPs) on AMR are ethically acceptable, and preferable to alternatives that might be less coercive or more effective, for instance. By ethically analysing case studies of coercive actions across countries, we can better inform policy in a context-specific manner. In this paper, I consider an example of coercive antimicrobial stewardship policy in Canada, namely restrictions on livestock farmers’ access to certain antibiotics for animal use without a vet’s prescription. I introduce and analyse ethical arguments that might plausibly justify coercive action in this case, including the harm principle and a duty of collective easy rescue. In addition, I consider the factors that might generally limit the application of those ethical concepts, such as challenges in establishing causation or evidencing the scale of the harm to be averted. I also consider specifics of the Canadian context in contrast to the UK and Botswana as example settings, to demonstrate how context-specific factors might mean a coercive policy that is ethically justified in one country is not so in another.

Machine-learning algorithms have the potential to revolutionise diagnostic and prognostic tasks in health care, yet algorithmic performance levels can be materially worse for subgroups that have been underrepresented in algorithmic training data. Given this epistemic deficit, the inclusion of underrepresented groups in algorithmic processes can result in harm. Yet delaying the deployment of algorithmic systems until more equitable results can be achieved would avoidably and foreseeably lead to a significant number of unnecessary deaths in well-represented populations. Faced with this dilemma between equity and utility, we draw on two case studies involving breast cancer and melanoma to argue for the selective deployment of diagnostic and prognostic tools for some well-represented groups, even if this results in the temporary exclusion of underrepresented patients from algorithmic approaches. We argue that this approach is justifiable when the inclusion of underrepresented patients would cause them to be harmed. While the context of historic injustice poses a considerable challenge for the ethical acceptability of selective algorithmic deployment strategies, we argue that, at least for the case studies addressed in this article, the issue of historic injustice is better addressed through nonalgorithmic measures, including being transparent with patients about the nature of the current epistemic deficits, providing additional services to algorithmically excluded populations, and through urgent commitments to gather additional algorithmic training data from excluded populations, paving the way for universal algorithmic deployment that is accurate for all patient groups. These commitments should be supported by regulation and, where necessary, government funding to ensure that any delays for excluded groups are kept to the minimum. We offer an ethical algorithm for algorithms—showing when to ethically delay, expedite, or selectively deploy algorithmic systems in healthcare settings.