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.