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Clinical Syntax: Diagnoses Without Subjects in AI-Powered Medical Notes

Abstract

This article examines the structural erasure of the patient as an active subject in clinical records generated by artificial intelligence systems. Automated outputs from Epic Scribe, GPT-4, and institutional medical note generators increasingly rely on impersonal constructions, nominalizations, and fragmented clauses that displace the patient from the syntactic center of medical discourse. The shift toward objectified formulations such as “bilateral opacities noted” rather than “the patient presents with” produces a discourse where agency and responsibility are structurally absent. Building on prior analyses of passive voice and subject deletion, the study introduces the Syntactic Opacity Index (SOI) as a formal measure to quantify the density of non-agentive structures in AI-authored notes. The corpus analysis demonstrates how opacity accumulates at the sentence level, rendering the clinical narrative less transparent and more difficult to attribute. Beyond linguistic critique, the article assesses the ethical and epistemic consequences of syntactic opacity in medicine, particularly regarding accountability, patient-centered care, and institutional responsibility. The findings suggest that AI-powered medical documentation does not merely accelerate administrative workflows but also reconfigures the grammar of care itself, demanding urgent attention to how language structures shape both diagnosis and responsibility. DOI Primary archive: https://doi.org/10.5281/zenodo.17184301 Secondary archive: https://doi.org/10.6084/m9.figshare.30187882 SSRN: Pending assignment (ETA: Q3 2025)

Keywords

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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