Artificial intelligence (AI) in healthcare is assumed to introduce risks that are not easily addressed by dominant philosophical models for thinking about responsibility. When an AI tool makes an error that results in patient harm, the question of who is responsible is rarely straightforward. Dominant models of responsibility work when harm can be traced to a single actor, but they fail in socio-technical systems where decisions and actions are distributed across multiple human and technological agents. Iris Marion Young's social connection model of responsibility offers a systematic way to rethink responsibility in these contexts. Rather than locating responsibility solely after harms occur, Young proposes a forward-looking account that attaches responsibility to those who participate in and benefit from the structural processes that produce harm. Young's framework organised around parameters of reasoning-power, privilege, interest, collective ability, and personal connection- provides an alternative way for understanding differentiated responsibilities across core actors in AI-driven healthcare.
The ontological status of health and disease remains a central problem in medical philosophy, commonly framed through tensions between naturalistic and normativist accounts. While useful for acute classification, static conceptions of disease contribute to "diagnostic fixation"-the persistence of clinical labels long after active pathological processes have stabilised. This article proposes a dynamic, five-dimensional framework that reconceptualises health not as categorical restoration, but as longitudinal viability. Drawing on Canguilhem's biological normativity and Ian Hacking's theory of looping effects, it argues that diagnostic revisability is a core ethical requirement in contemporary healthcare. By shifting the clinical focus from categorical restoration to longitudinal viability, the framework offers a pathway to mitigate biographical surveillance. The article concludes with practical guidelines for the ethical retirement of diagnostic labels while addressing clinical concerns related to liability and institutional inertia.
This paper critically reviews Sparrow et al.’s notion of the “hermeneutic burden” placed upon clinicians by the demand for explainable artificial intelligence (XAI) in the context of adaptive machine learning (ML) systems. While Sparrow et al. highlight important additional labour that may be required of clinicians, this response argues that framing explanation primarily in terms of such a burden obscures its overall ethical significance. This paper therefore offers a supplementary account of the interpretive work associated with XAI in medicine that places it within existing models of the patient–clinician relationship. In particular, Emanuel and Emanuel’s influential typology consisting of four models of the patient–physician relationship is used to extract possible justifications for the responsibility to grasp and explain not only patients’ values and conditions but also ML outputs. This allows us to distinguish between ‘hermeneutic burden’ and ‘hermeneutic responsibility’ and emphasise that explanation in medicine is not an incidental task but part of a clinician’s professional role, particularly on ‘interpretive’ and ‘deliberative’ models. The paper thus argues that viewing explanation as a hermeneutic responsibility linked to patient autonomy clarifies the ethical significance of XAI in terms of both the grounds and scope of clinicians’ responsibilities. At its core, the ethical challenge raised by XAI in clinical practice concerns not only the burdens it may impose on clinicians but also the evolution of clinicians’ traditional interpretive duties in the novel context of ML-mediated care.
A controlled human infection study is the deliberate exposure of a healthy volunteer to an infectious pathogen under a strict controlled environment. Although there has been noticeable contribution of such studies to the biomedical research involving understanding of disease pathogen interaction, vaccine development, etc, they are in direct contradiction of core ethical principles such as non-maleficence. The major ethical dilemma lies in the deliberate exposure without certain direct benefits. This narrative review discusses the ethical challenges and need for robust ethical framework taking in account recent international guidelines, debates on trial design and placebo use, and evidence-based analyses of trial conduct and reporting. Analysis of ethical principles such as proportionality, respect for persons' decision, and social value has been done through this article with emphasis on challenge strain selection, participant selection, control group design and adverse event monitoring. Special attention was paid to CHIS in low-and middle-income countries, where potential direct benefits, and issues like justice and capacity building complicate standard ethical convention. We further examined the CHIS status in India despite its substantial infectious disease burden, placing this gap within the country's evolving ethical and regulatory landscape. We argue that CHIS can be morally acceptable with stringent context-sensitive ethical standards, and transparency in reporting, thereby prioritising public trust and equitable benefit sharing.
