Human and large language model judgments of cognitive impairment from language: An explainable artificial intelligence approach.

ABSTRACT:

Introduction

We examine how non-expert humans (young adults unfamiliar with dementia) and large language models (LLMs) perceive dementia in transcribed texts-recognizing signs that may indicate cognitive decline. Human perception is important, as it is often the driver for seeking medical evaluation. LLM perception is equally interesting given their potential as screening tools.

Methods

Humans and LLMs intuitively judged whether transcribed picture descriptions came from dementia patients or healthy controls. We represented texts using high-level, expert-guided features and used logistic regression to model perceptions and analyze coefficients.

Results

Human judgments are inconsistent, relying on a narrow and sometimes misleading set of cues. LLMs use a richer, more c

SUBMITTER: Zadok M 

PROVIDER: S-EPMC12829547 | biostudies-literature | 2026 Jan-Mar

REPOSITORIES: biostudies-literature