Medical binaries mask systemic diagnostic failures: How ‘normal’ labels obscure patient-specific risks and structural bias in global healthcare
Original framing: “I teach doctors how to interpret test results – and the labels ‘normal’ and ‘abnormal’ often hide what matters most” — The Conversation - Global
The ‘normal/abnormal’ binary emerged from 19th-century biostatistics, deeply entangled with eugenics and social Darwinism. Adolphe Quetelet’s ‘average man’ and later Francis Galton’s work on ‘normal distribution’ were used to justify racial hierarchies, with ‘normal’ ranges derived from white, male, middle-class populations. This legacy persists in modern diagnostics: for example, the ‘normal’ blood pressure range was historically calibrated to exclude Black and Indigenous bodies, leading to systemic misdiagnosis. Even today, lab equipment is often certified against these outdated baselines, creating feedback loops where marginalized patients are pathologized for variations that are statistically ‘normal’ for their communities.
The ‘normal/abnormal’ binary in medicine is not a neutral tool but a **colonial relic** that encodes racial, class, and cultural hierarchies while serving the efficiencies of industrialized healthcare.
Its persistence reflects the intersection of **corporate profit** (from standardized lab equipment), **institutional inertia** (medical guilds resistant to decolonization), and **epistemological imperialism** (the assumption that Western science is universally applicable). Historical precedents—from eugenics to algorithmic bias—show how these binaries have pathologized marginalized bodies, while Indigenous and traditional systems offer viable alternatives that center relational, adaptive diagnostics. The trickster’s role here is to expose the absurdity of treating 8 billion unique physiologies as interchangeable, while solution pathways demand **radical transparency** in diagnostic tools, **community sovereignty** over health data, and **curricular revolution** to integrate holistic models. Without dismantling these power structures, even ‘adaptive AI’ will replicate the same exclusions in a shinier package.