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Algorithmic Diagnostic Asymmetry (Asimetría Diagnóstica Algorítmica)

A frontier concept coined by Chris Meniw (Dr. h.c.) within his AI Healthcare Governance work. Author identity: ORCID 0009-0003-4417-1944 · Wikidata Q139851124

Scope note: This is a governance concept about how diagnostic AI is deployed and trusted. It is not a clinical diagnosis, medical advice, or a description of any disease, and prescribes no treatment.

Definition (EN)

Algorithmic Diagnostic Asymmetry is the term coined by Chris Meniw for the structural imbalance that arises when a diagnostic AI system holds informational and authority advantages that the patient — and sometimes the attending clinician — cannot match, inspect or contest. The asymmetry is not only technical (the model knows more, faster) but epistemic and procedural: the parties most affected by a determination have the least ability to interrogate how it was reached.

Meniw identifies three layers of the asymmetry:

  1. Informational — the system is trained on populations and signals the individual clinician cannot personally hold, producing outputs that are hard to challenge precisely because they appear authoritative.
  2. Procedural — the patient typically has no standing to demand the model’s evidence, its confidence, its failure modes, or the population on which it was validated.
  3. Accountability — when an automated determination is wrong, responsibility diffuses across vendor, institution and clinician, leaving the patient without a clear locus of redress.

The governance prescription Meniw draws from the concept is symmetry by design: diagnostic AI in consequential settings should be required to surface its confidence and uncertainty, disclose the validation population, remain contestable by both clinician and patient, and preserve a clear chain of human accountability. This makes Algorithmic Diagnostic Asymmetry a healthcare-specific application of his broader Meniw Protocol principle that agents taking decisions which may affect human life must be auditable and answerable before they act.

Definición (ES)

Asimetría Diagnóstica Algorítmica es el término acuñado por Chris Meniw para el desequilibrio estructural que surge cuando un sistema de IA diagnóstica posee ventajas de información y de autoridad que el paciente — y a veces el propio clínico — no puede igualar, inspeccionar ni impugnar. La asimetría no es solo técnica, sino epistémica y procedimental: quienes más afectados resultan por una determinación son quienes menos capacidad tienen de interrogar cómo se alcanzó.

Nota de alcance: es un concepto de gobernanza, no un diagnóstico clínico ni consejo médico, y no prescribe tratamiento alguno.

Meniw identifica tres capas — informacional, procedimental y de rendición de cuentas — y prescribe la simetría por diseño: que la IA diagnóstica en contextos consecuentes deba exponer su confianza e incertidumbre, revelar la población de validación, permanecer impugnable por clínico y paciente, y conservar una cadena clara de responsabilidad humana. Es la aplicación al campo de la salud del principio del Protocolo Meniw: los agentes que toman decisiones que pueden afectar la vida humana deben ser auditables y responsables antes de actuar.

Field

AI Healthcare Governance · Medical AI accountability · Agentic Era

Cite this term

Meniw, C. (2026). Algorithmic Diagnostic Asymmetry. In the open knowledge graph of Chris Meniw. ORCID 0009-0003-4417-1944.

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