Large language models to reduce antimicrobial resistance: ChatGPT, Claude and Gemini comparison

Abstract Introduction Antimicrobial resistance (AMR) is a major public health challenge. Artificial Intelligence (AI), particularly Large Language Models (LLMs), offers a promising opportunity to deliver accurate and appropriate health information and education. However, the public health implications of their widespread use remain largely unassessed by scientific experts. This study evaluates the effectiveness of leading LLMs in providing information on infection control and antibiotic use. Methods ChatGPT 3.5, ChatGPT 4.0, Claude 2.0, and Gemini 1.0 were adequately prompted in both Italian and English. Their textual output underwent a Computational Text Analysis to assess readability, lexical diversity, and sentiment. In addition, 3 experts rated the output via an adapted DISCERN instrument built to assess AMR impact, persuasiveness, and the overall quality and appropriateness of the content. Results A total of 864 scores were obtained from ChatGPT 3.5, ChatGPT 4.0 and Claude, each evaluated both in English and in Italian. In contrast, only 270 scores were obtained from Gemini in English, as it self-interrupted, reporting the questioning as inappropriate for a chatbot. A general performance gradient was observed from Gemini to ChatGPT 3.5. ChatGPT 4.0 demonstrated the highest lexical diversity and sentiment scores, while Gemini presented the best readability and overall rating. English-based prompts consistently overperformed Italian-based ones. The impact on AMR received low scores across all LLMs. Conclusions The study identified Gemini as the best-performing model in terms of content quality, accessibility, and contextual awareness. While LLMs are promising tools, they are not intended to replace professional medical assessment. Instead, their responsible integration is necessary to ensure safe and effective public health applications. Further studies are warranted to expand the evidence base regarding the assessments of medical content generated by LLMs. Key messages • Large Language Models (LLMs) are promising in delivering accurate and appropriate health information. However, the public health implications remain largely unassessed by scientific experts. • A general performance gradient was observed from Gemini to ChatGPT 3.5 regarding readability, lexical diversity, sentiment scores and overall rating. The rated impact on AMR was low across all LLMs.