Dataset of clinical cases, AI response and experts response from the research: Antimicrobial Stewardship in the Era of AI: A Head-to-Head Comparison of a Machine Learning HTL Algorithm and Large Language Models in Real-World Infectious Disease Cases

The following dataset contains information used for the study titled: Antimicrobial Stewardship in the Era of AI: A Head-to-Head Comparison of a Machine Learning HTL Algorithm and Large Language Models in Real-World Infectious Disease Cases. This dataset contains 88 clinical cases of urinary tract infections extracted from anonymized medical records (to exclude patients’ personal data). It also includes portions of the antibiotic susceptibility test reports from each record. Based on this data, prompts were created and presented to the artificial intelligence models for analysis, with the aim of obtaining recommendations for initial and secondary antimicrobial treatment. We examined whether there was agreement or disagreement among the models’ responses, as well as the type of disagreement analyzed. Finally, we present the categorization of the responses provided by the three infectious disease experts, compared to the models’ responses.