Evaluating Deepseek’s Capability in Solving Higher-order and Multiple-choice Questions in Microbiology: A Cross-sectional Observational Study

Introduction: The Chinese generative artificial intelligence (AI) model, DeepSeek-R1, has profoundly taken over the AI market since its launch. Large language models (LLMs) can generate humanoid responses and conduct reasoning tasks by utilising deep neural networks that have been trained on large textual datasets. Materials and Methods: Using Deepseek-R1, we conducted a cross-sectional study where 60 higher-order questions and 60 multiple-choice questions (MCQs) from microbiology of the MBBS curriculum were entered as prompts in the online AI chatbot. Three academics from the department evaluated the responses and a statistical analysis was performed. Results: The average of the scores of the three evaluators was taken as the final score for analysis. Non-parametric statistical methods were implemented. A one-sample median test was used to evaluate the total results, which was significant at P < .05. A non-significant difference was observed when the scores by the three evaluators were compared by the Kruskal-Wallis H test. One-tailed binomial test showed 98.33% accuracy in the MCQs. Conclusion: DeepSeek-R1 is a user-friendly, free-of-cost AI model to improve information retrieval through the application of deep learning methods. DeepSeek has excellent database in microbiology with lucid and elaborative explanations.