ArticleBMJ (Clinical research ed.)2024
Age against the machine-susceptibility of large language models to cognitive impairment: cross sectional analysis.
Article in BMJ (Clinical research ed.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- DiAbot: A Conversational AI System Coupling Large Language Models with an Interpretable Decision Tree for CDR-Style Dementia Screening.Bioengineering (Basel, Switzerland) · 2026Article
- Deficient executive control in transformer attention.PNAS nexus · 2026Article
- "The Machine Will See You Now": A Clinician's Perspective on Artificial "Intelligence" In Clinical Care.Movement disorders clinical practice · 2025Article
- AI, universal basic income, and power: symbolic violence in the tech elite's narrative.Frontiers in artificial intelligence · 2025Article
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Abstract
objectiveTo evaluate the cognitive abilities of the leading large language models and identify their susceptibility to cognitive impairment, using the Montreal Cognitive Assessment (MoCA) and additional tests.
designCross sectional analysis.
settingOnline interaction with large language models via text based prompts.
participantsPublicly available large language models, or "chatbots": ChatGPT versions 4 and 4o (developed by OpenAI), Claude 3.5 "Sonnet" (developed by Anthropic), and Gemini versions 1 and 1.5 (developed by Alphabet). ASSESSMENTS: The MoCA test (version 8.1) was administered to the leading large language models with instructions identical to those given to human patients. Scoring followed official guidelines and was evaluated by a practising neurologist. Additional assessments included the Navon figure, cookie theft picture, Poppelreuter figure, and Stroop test.
main outcome measuresMoCA scores, performance in visuospatial/executive tasks, and Stroop test results.
resultsChatGPT 4o achieved the highest score on the MoCA test (26/30), followed by ChatGPT 4 and Claude (25/30), with Gemini 1.0 scoring lowest (16/30). All large language models showed poor performance in visuospatial/executive tasks. Gemini models failed at the delayed recall task. Only ChatGPT 4o succeeded in the incongruent stage of the Stroop test.
conclusionsWith the exception of ChatGPT 4o, almost all large language models subjected to the MoCA test showed signs of mild cognitive impairment. Moreover, as in humans, age is a key determinant of cognitive decline: "older" chatbots, like older patients, tend to perform worse on the MoCA test. These findings challenge the assumption that artificial intelligence will soon replace human doctors, as the cognitive impairment evident in leading chatbots may affect their reliability in medical diagnostics and undermine patients' confidence.
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