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June 1, 2026NEJM AI0 citations

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

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AVAlfredo Vannacci

Key Points

  • The aim is to improve terminology for AI errors in clinical contexts, advocating for better understanding and trust.
  • Addressed the terminology debate regarding AI error descriptions.
  • Proposed a functional typology of errors distinguishing between ordinary and delusional confabulation.
  • Discussed the implications of these terms in clinical practice.
  • Argued that 'hallucination' misdirects understanding while 'confabulation' is more accurate.
  • Identified practical implications for how terminology impacts clinical trust in AI technologies.

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.

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Cite This Study

Alfredo Vannacci (2026) studied this question.

synapsesocial.com/papers/6a1d20f302fbce91306373a3https://doi.org/10.1056/ailtr2600282
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