Assessing the role of Large Language Models in Mild Cognitive Impairment Management: A Bilingual Analysis Comparing ChatGPT, Gemini, and Kimi
Year: 2025
Type: article
Abstract: BACKGROUND: The demand for accessible and actionable information to support mild cognitive impairment (MCI) management is growing, particularly for care partners and non-psychiatric healthcare professionals seeking reliable guidance. Large Language Models (LLMs) have shown potential across various domains but have not been systematically evaluated in addressing MCI-related queries. A critical gap exists in understanding LLM performance across different aspects of dementia care and the impact of language differences on their effectiveness. This study addresses these gaps by assessing LLM-generated responses in both English and Chinese, providing essential insights into their capabilities and limitations. METHOD: Healthcare professionals (N = 5) and care partners (N = 5) were recruited from diverse clinical and caregiving backgrounds. A set of 72 open-ended questions across four MCI domains-Symptoms, Treatment, Care partner support, and Nursing&rehabilitation-was developed. Responses from three LLMs (ChatGPT-4, Gemini, Kimi) were evaluated by a five-point accuracy scale using a double-blind design. Ratings were based on accuracy, comprehensibility, specificity, and actionability. Statistical analyses included ICCs for reliability and Mann-Whitney U tests to compare responses. RESULT: Among the four domains of MCI management, ChatGPT had the best performance in symptoms-related queries (average score: 4.03, all p < 0.001). All three LLM-generated responses demonstrated better alignment with healthcare professionals' requirements, achieving significantly higher concordance in comprehensibility (HP:4.24 vs CP:4.06, p <0.001) and actionability (HP:4.04 vs CP:3.72, p <0.001). Performance parity was observed in accuracy (CP:4.32 vs HP:4.27) and specificity (CP:3.79 vs HP:3.83) with no statistical significance. Additionally, English responses surpassed Chinese responses in accuracy (4.32 vs. 4.26, p = 0.056), comprehensibility (4.23 vs. 4.10, p < 0.001), and specificity (3.96 vs. 3.66, p < 0.001), while actionability scores showed no significant difference (3.94 vs. 3.89, p = 0.239). CONCLUSION: This study demonstrates LLMs' proficiency in symptom-related inquiries and stronger alignment with healthcare professionals' operational needs versus care partners' accuracy priorities. English responses outperformed Chinese due to corpus disparities, highlighting needs for language-specific optimization. Findings underscore LLMs' clinical potential, urging enriched Chinese medical corpora and specialized model development to address care partners' unmet requirements.
Source: Alzheimer s & Dementia
Authors: Yexuan Xiao, Qianhui Pan, Nan Jiang
Institution: Tsinghua University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,200
OpenAlex ID: W7117325812
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