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

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)

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Publication Details

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Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-23
DOI
https://doi.org/10.5281/zenodo.17184300
Primary Topic
Geochemistry and Geologic Mapping
Type
article
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article

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

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

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

article en

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)

Zenodo (CERN European Organization for Nuclear Research)
Universidad de la República de Uruguay (UY), Universidad de la Empresa (UY), University of Palermo (AR), Universidad La República (CL)
Quality Education
Openalex Percentile: Top 29%
Geochemistry and Geologic Mapping
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Clinical Syntax: Diagnoses Without Subjects in AI-Powered Medical Notes · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS