Journal of Medicine and Philosophy最新文献

Do healthcare workers have a duty to treat contagious patients, even when it poses risks to their own health and lives during a pandemic? This article explores various justifications proposed in the literature to support such a duty. However, it contends that none of these provides a strong enough basis for establishing an absolute duty to treat-although it acknowledges that the bar of justification may be raised when working on more clear and explicit conditions in contracts and codes of ethics, among others. Furthermore, even if such a duty were acknowledged, it must be weighed against healthcare workers' other duties toward their families, co-workers, and personal well-being. Moreover, the duty to treat is argued to be contingent on the circumstances in which healthcare professionals operate, including access to adequate personal protective equipment provided by their institutions. It would have to be balanced against their right to safe working conditions. Within this context, the duty to treat is inherently tied to the preparedness of the State, healthcare systems, or institutions to effectively respond to emergencies.

This paper critically evaluates Matthew Burch's interpretations and critiques of the phenomenological account of health and illness, which are predominantly situated within the realm of static phenomenology within Husserl's framework, thereby neglecting the potential insights offered by genetic phenomenology. The primary focus of this paper is to explore genetic phenomenology in order to present an alternative interpretation of PHI. It argues that illness experience involves subjectivity, intersubjectivity, and objectivity, unified within a structural interdependence. Additionally, normality comprises subjective, intersubjective, and objective dimensions, reflecting its multifaceted nature. It encompasses both a pregiven aspect and a constitutive process. Moreover, the distinction between the lived body and the physical body is a result of first-person subjectification and third-person objectification perspectives. These perspectives mutually complement and intertwine, where bodily transparency and bodily conspicuousness do not necessarily conflict.

This essay compares representations of transgender people in Western mass and social media with data drawn from studies on transgender individuals. Three differences between the surveyed data and the media representations stand out: (1) while Western media focus on male-to-female (M-F) individuals, most transgender people in Western societies today are female-to-male (F-M). (2) Western media representations of transgender individuals highlight glamorous, successful people. Empirical data show that the socioeconomic status of transgender individuals in Western societies tends to be lower than that of nontransgender people. (3) In Western media, the transitioning process of transgender people is often portrayed as a successful soteriological journey of becoming one's "true self." Medical surveys show that transgender people suffer from psychological and physical problems both before and after transitioning. It is concluded that the disparity between the empirical data and the media narratives on transgender people is due to the persistence of neoliberal narratives in Western media.

In 1981, Edmund Pellegrino and David Thomasma published A Philosophical Basis of Medical Practice. In this work, they situated the process of clinical judgment in the clinical encounter between an individual doctor and their patient. The encounter revolves around three questions: What can be wrong? What can be done? And what should be done for this patient? They analyzed the complete process of clinical reasoning involving both technical and ethical aspects. Pellegrino and Thomasma's subsequent work focused more on professionalism and ethics, while more recent analysis of clinical decision-making has been in the realm of psychology rather than along philosophical lines, particularly in the use of dual-process theory. Here we seek to review Pellegrino and Thomasma's analysis and to reintegrate the technical and ethical aspects of clinical reasoning.

Serious illness communication is at the core of palliative care, aligning care with patient preferences and improving patient and family experience. Artificial Intelligence (AI) methods have increasingly been applied to palliative care and provide promising opportunities for measuring and enhancing communication (e.g., capturing speech patterns and delivering feedback). Yet, given known disparities in palliative care and the limitations afforded by our natural communication datasets, this task must be approached with caution. Focusing on the study of communication, we consider assumptions that may be baked into our models (e.g., in data, definitions, measurements, and outcomes) and ways to mitigate potential harm across stages of model development-from setting priorities for AI research and applications in our field, to conducting new data collection efforts which are inclusive and more representative, to incorporating patient-family feedback. Transparency and thoughtfulness in this line of innovation may help us leverage AI to provide more equitable, higher-quality serious illness care (see Figure 1). Fig. 1. Visual abstract.

Professional judgment is of contested value today. Some argue that the current availability of tools for aligning decisions with evidence-based standards implies that individual judgment should be limited as much as possible. This article argues to the contrary: professional judgment remains a precondition for responsible practice. Nevertheless, increased epistemic dependence-the turn to second-hand medical knowledge-alters the domains of judgment. As first-order evidence has become overwhelming and opaque to practitioners, they need intelligent ways of placing their trust, of integrating different kinds of epistemic tools, and taking responsibility for consequences. The article suggests how these tasks can be seen as a complement to the original ambition of the evidence movement of promoting research literacy.

The recent developments of medical AI systems (MAIS) open up questions as to whether and to what extent MAIS can be modeled to include empathetic understanding, as well as what impact MAIS' lack of empathetic understanding would have on its ability to perform the necessary critical analyses for reaching a diagnosis and recommending medical treatment. In this article, we argue that current medical AI systems' ability to empathize with patients is severely limited due to its lack of first-person experiences with human interests and that efforts to correct for this deficit-by having MAIS interpret patients' medical and non-medical interests-will encounter significant obstacles. Finally, we demonstrate how MAIS' lack of empathy is likely to hinder its performance in crucial aspects of the processes through which useful medical diagnoses are reached and through which appropriate treatment recommendations for patients are determined.

I argue that true informed consent is impossible to obtain for certain medical procedures in which epistemic transformation occurs. Cases in which undergoing a procedure itself provides new experiential information, that is, phenomenal knowledge (what I call "knowledge-what-it's-like"), true informed consent for that procedure cannot be attained from knowing facts about the procedure ("knowledge-that") alone. If epistemically transformative medical procedures indeed undermine informed consent as I argue they do, I suggest that there are important implications for the decision-making of patients considering these procedures (e.g., chemotherapy, invasive surgeries, cochlear implants, gender-affirming procedures). Rather than solely communicating biological, clinical, and epidemiological facts about procedures, clinicians should supplement pre-procedure counseling with previous patient testimonials or even virtual/augmented reality to compensate (albeit partially) for the "knowledge-what-it's-like" that is absent prior to undergoing epistemically transformative medical procedures. Although these interventions may not (accurately) convey what it is like to undergo the procedure, they address the traditionally under-explored experiential aspect of medical treatments in medical decision-making from the patient's perspective.

Enthusiasm about the use of artificial intelligence (AI) in medicine has been tempered by concern that algorithmic systems can be unfairly biased against racially minoritized populations. This article uses work on racial disparities in knee osteoarthritis diagnoses to underline that achieving justice in the use of AI in medical imaging requires attention to the entire sociotechnical system within which it operates, rather than isolated properties of algorithms. Using AI to make current diagnostic procedures more efficient risks entrenching existing disparities; a recent algorithm points to some of the problems in current procedures while highlighting systemic normative issues that need to be addressed while designing further AI systems. The article thus contributes to a literature arguing that bias and fairness issues in AI be considered as aspects of structural inequality and injustice and to highlighting ways that AI can be helpful in making progress on these.

In this article, we focus on questions of agency in emerging technologies related to decision-making in medicine. We discuss three principles that were subsumed when bioethics embraced principlism: consent, confidentiality, and veracity. We argue that the advent of artificial intelligence and its employment within health care, impacts the physician-patient relationship in a way that its inclusion in other areas does not. In particular, we take up ethical dilemmas caused by AI related to trust, and illustrate how reflection on these subsumed principles helps to critique accurately policies related to the use of AI in health care and to navigate dilemmas associated with a loss of trust. We conclude by contrasting these principles with the proposed "five principles" system for AI, highlighting some areas of agreement, but also showing where consent, confidentiality, and veracity are necessary additions for ethically employing AI.