This Letter responds to the ongoing debate on the terminology for large language model errors by arguing that “hallucination” mislocates the error in the perceptual domain and that “confabulation” is a better-calibrated conceptual borrowing. It further proposes a functional typology distinguishing ordinary from delusional confabulation, with practical implications for clinical settings.

Borrowing Carefully — The Words We Choose for AI Errors Shape Clinical Trust / Vannacci, Alfredo. - In: NEJM AI. - ISSN 2836-9386. - ELETTRONICO. - 3:(2026), pp. 0-0. [10.1056/ailtr2600282]

Borrowing Carefully — The Words We Choose for AI Errors Shape Clinical Trust

Vannacci, Alfredo
2026

Abstract

This Letter responds to the ongoing debate on the terminology for large language model errors by arguing that “hallucination” mislocates the error in the perceptual domain and that “confabulation” is a better-calibrated conceptual borrowing. It further proposes a functional typology distinguishing ordinary from delusional confabulation, with practical implications for clinical settings.
2026
3
0
0
Vannacci, Alfredo
File in questo prodotto:
File Dimensione Formato  
Vannacci NEJM AI 2026.pdf

Accesso chiuso

Tipologia: Pdf editoriale (Version of record)
Licenza: Tutti i diritti riservati
Dimensione 154.04 kB
Formato Adobe PDF
154.04 kB Adobe PDF   Richiedi una copia

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1473092
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact