Exploring large language models as a prescription decision support tool for rational antibiotic use: A dual-framework analysis using standardized examinations and real-world clinical cases

Background Irrational antibiotic use remains a critical challenge in clinical pharmacy practice, driving antimicrobial resistance. Large language models (LLMs) are increasingly explored as tools to support medication use processes, yet their practical utility in antimicrobial stewardship—particularly in prescription evaluation—remains underexamined. Objective This cross-sectional study aimed to explore the potential of three mainstream Chinese LLMs (DeepSeek, DouBao, Qwen) as auxiliary tools in rational antibiotic use, focusing on their performance in both knowledge-based and practice-based pharmacy tasks. Methods A dual-framework evaluation was conducted. First, a standardized 100-point examination based on national antimicrobial guidelines assessed the models' foundational knowledge relevant to clinical pharmacy. Second, 20 real-world outpatient antibiotic prescriptions with documented irrational issues were analyzed by each model. Responses were independently evaluated by two clinical pharmacists using a five-point scoring scale. Inter-rater agreement was assessed using Cohen's weighted Kappa. Results In the standardized examination, the models achieved an average score of 86.0 ± 2.0 points, with relatively lower performance in empirical therapy modules. In the clinical prescription analysis, the average score was 92.3 ± 3.5 points (range: 89–96), with good inter-rater consistency (Cohen's weighted Kappa = 0.89). All models demonstrated stronger performance in identifying clinical logic contradictions than in handling institution-specific compliance rules. Conclusion Mainstream Chinese LLMs show promising potential as exploratory AI in antimicrobial stewardship, particularly in supporting prescription logic review within the medication use process. Their stronger real-world performance supports human–AI collaboration. Future work should focus on integrating these tools into clinical workflows as auxiliary screening aids.