Aug 2026· Science and Engineering Ethics· Vol 32· 0 citations· 59 references
MedicineComputer Science
TL;DR
The paper draws on the moral ecology of care and shows that algorithmic systems can reconfigure epistemic authority, redistribute responsibility, and risk marginalising context-sensitive and relational dimensions of care and argues that AI systems are better understood as socio-technical mirrors that reflect and amplify existing human values, institutional arrangements, and power asymmetries.
Abstract
Artificial intelligence (AI) is increasingly promoted as a tool to enhance clinical decision-making and thus improve the quality of healthcare. While much of the emerging scholarship on AI and healthcare in Africa has focused broadly on opportunities and systemic challenges, what remains underexplored is the specific application of AI to clinical decision-making. This paper contributes to addressing this gap by offering a conceptual and critical analysis of AI-based clinical decision support systems (AI-CDSS) in African contexts. Drawing on philosophical accounts of medical reasoning and relational moral frameworks such as Ubuntu, the paper draws on the moral ecology of care and shows that algorithmic systems can reconfigure epistemic authority, redistribute responsibility, and risk marginalising context-sensitive and relational dimensions of care. The paper further argues that AI systems are better understood as socio-technical mirrors that reflect and amplify existing human values, institutional arrangements, and power asymmetries. Moving beyond the algorithm, it proposes a shift toward relational and context-sensitive AI governance, including the development of relational impact assessments, the redistribution of responsibility across the AI lifecycle, and the co-production of knowledge with local stakeholders. While focusing on African clinical contexts, the analysis offers broader insights for global debates on AI ethics and clinical decision-making.
This paper distinguishes technical reliability from trust in AI-supported clinical decision-making, arguing that the two belong to distinct registers that current debates tend to elide and proposes replacing the expression "trustworthy AI" with "technically reliable AI," a distinction that relocates the conditions of t...
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It is concluded that a rights-sensitive use of AI requires procedural safeguards embedded in institutional design: functional transparency, documented dialogue, renewable consent, protected deviation from automated outputs, and accessible routes of contestation.
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A narrative review of the evolving image of XAI as a tool to create trustworthy clinical decision support systems and how it can be performed based on the concepts of transparency, interpretability and moral decision-making concludes that explainability is a fundamental component to the use of AI in everyday clinical p...
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It is argued that traditional clinical risk-management models are structurally insufficient to address opaque algorithmic bias and presented, a conceptual, multidimensional governance framework designed to prevent the codification of human ageism into AI infrastructure is presented.
Eyal Cohen, Yehuda Adler, R. Nissanholtz-Gannot· Healthcare· 0 citations
This article proposes seven questions that clinicians can run through to evaluate any clinical AI tool in the time it takes to read an abstract, alongside a traffic-light schema for matching oversight to risk and a short list of demands clinicians should make of vendors and institutions.
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A review examines how free full-text PubMed literature describes the real-world implementation of AI in healthcare and identifies the technical, organizational, ethical, and social conditions that shape adoption.
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