When AI Writes the Letters: Recognizing Synthetic Authorship Patterns in Medical Publishing

The rapid integration of generative artificial intelligence into scientific publishing is reshaping how academic text can be produced, revised, and scaled. While transparent and limited use of AI for language support may be acceptable, a new structural vulnerability may be emerging in medical publishing: the large-scale production of short, plausible, and weakly individualized correspondence across multiple specialties. In this viewpoint, we describe and conceptualize a pattern that may be termed synthetic authorship, defined not as undisclosed AI use alone, but as a reproducible mode of scholarly output structurally facilitated by automation. We focus particularly on letters to the editor, a format that combines brevity, rapid editorial handling, and formal indexation, and may therefore be especially exposed to this phenomenon. Based on recurring patterns observed in PubMed-indexed literature, including unusually high publication velocity, abrupt thematic dispersion, and stylistic uniformity across unrelated domains, we argue that such outputs may challenge the authenticity, epistemic value, and editorial function of scientific correspondence. We do not present empirical proof of misconduct, but rather outline a conceptual framework for understanding this emerging risk and propose proportionate editorial safeguards, including cross-domain pattern detection and contextual assessment of authorship coherence. As AI lowers the threshold for generating domain-plausible commentary at scale, scientific publishing must adapt its integrity frameworks accordingly. In this context, vigilance toward synthetic authorship may become an essential component of editorial responsibility and post-publication quality control.