ArticleMedicine, health care, and philosophy2026
Clarifying the muddle: towards a comprehensive taxonomy of cognitive biases in medicine.
Article in Medicine, health care, and philosophy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Diagnostic errors in critically ill patients: challenges, contributing factors, and improvement strategies through the SEIPS framework.Intensive care medicine · 2026Article
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2 authors.
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Abstract
Cognitive biases are widely discussed in the medical literature as systematic deviations from rational judgment and as significant contributors to diagnostic error. However, the prevailing view of cognitive biases as inherently epistemically detrimental may be challenged by recent research highlighting their potential utility. In this paper, we argue that the impact of cognitive biases in medicine depends not only on their internal cognitive structure, but also on the ecological characteristics of the medical environments in which they operate. We develop a conceptual framework that distinguishes between three types of cognitive biases: (1) those that are epistemically reliable (and practically useful) in so-called "safe cognitive environments", where ecological informational structures reliably support the use of "fast and frugal" heuristics; (2) those that are practically useful in "pragmatically permeable contexts" such as emergency care or public health, where time pressure, uncertainty, or resource constraints justify decisions that are epistemically suboptimal but action-guiding; and (3) those that are neither epistemically nor pragmatically defensible, and thus constitute clear targets for mitigation. By offering this taxonomy, we aim to clarify when and why cognitive biases hinder or support medical reasoning and practical actions. Our analysis also emphasizes the need to reframe some traditional assumptions in medical epistemology and education. Rather than attempting to eliminate all cognitive biases, we advocate for a more nuanced, context-sensitive, and ecologically informed approach in medicine. We conclude by outlining the implications of this framework for improving diagnostic accuracy, professional training, and institutional practices in healthcare.
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