ArticleJMIR AI2026
AI Chatbot Answers for Drug Dosing Adjustments According to Renal Function in Geriatric Patients Using the New Scoring System (AI Quality Output Score): Cross-Sectional Study.
Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Preventable adverse drug reactions in geriatric patients are caused by overdosing, especially in cases of impaired renal function. Artificial intelligence (AI) chatbots are being discussed as tools to generate drug information, which can adjust drug dosing and prevent subsequent adverse drug reactions based on individualized patient data. However, the question arises as to the extent to which such AI chatbots can withstand scientific evaluation in this task. Objective: We newly developed and validated the AI quality output score (AQUOS, ranging from 0% to 100%) to assess the quality of AI chatbot answers. We investigated whether AQUOS depends on (1) renal function, (2) medication complexity, (3) prompting language (English and German), and (4) whether the answers are reproducible (assessed at 2 independent times). Additionally, we assessed the potential for harm. Methods: In a standardized prompt, we asked 4 AI chatbots (ChatGPT, Copilot, Gemini, and Scite) whether the medication of 100 geriatric patients with polymedication at discharge should be adjusted according to their renal function. We prompted drug-related queries in 2 languages and at 2 times to assess AI chatbot answers, and we scored the generated outputs based on AQUOS. Additionally, we assessed possible harm from the AI chatbot answers using the World Health Organization definition "The conceptual framework for the international classification for patient safety." Results: We analyzed 1600 AI chatbot answers, with AQUOS values ranging from -19.0% to 95.2%, depending on the chatbot. We found that AQUOS declined with decreasing renal function (ChatGPT: -0.215; P=.03) and increasing medication complexity (Scite: -0.239; P=.02). Possible harm also correlated with more complicated patient statuses (lower kidney function and higher medication complexity) across all chatbots. Overall scores were up to 4.8% higher in English than in German prompting. The AI chatbot answers were highly reproducible. Conclusions: In renal drug dosing, the quality of AI chatbot answers declined as renal function decreased and medication complexity increased. Even the highest AQUOS achieved is insufficient for deploying AI chatbots in the high-risk health care sector.
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