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Objective: Large language models (LLMs) have demonstrated mixed results in their ability to pass various specialist medical examination and their performance within the field of emergency medicine remains unknown. Methods: We explored the performance of three prevalent LLMs (OpenAI's GPT series, Google's Bard, and Microsoft's Bing Chat) on a practice ACEM primary examination. Results: All LLMs achieved a passing score, with scores with GPT 4.0 outperforming the average candidate. Conclusion: Large language models, by passing the ACEM primary examination, show potential as tools for medical education and practice. However, limitations exist and are discussed. [ABSTRACT FROM AUTHOR]

Background The official prescribing information document distributed with a prescription drug is a key source of safety information, but it may include excessive or insufficient details. Objectives To compare prescribing information approved by the US Food and Drug Administration with the UK, Canada and Australia to identify content differences in safety warnings. Methods For 20 top-selling prescription drugs, we used an automated natural language processing tool to calculate the number and severity of reported adverse drug reactions (ADRs). We fit hierarchical Poisson models and included fixed effects for other prescribing information characteristics. Separately, we analysed the appearance and content of 'black box' warnings. Results There was substantial variation in safety content of approved prescribing information. Canada had the highest median ADRs per drug (138 (IQR 86-234)) and the UK had the lowest (84 (IQR 51-111)). The number of ADRs reported was on average 50% higher in Canada compared with the USA (ratio of ADRs/ document: 1.5, 95% CI 1.4 to 1.6, p<0.001). By contrast, there were on average 15% fewer ADRs listed in the UK compared with the USA (ratio of ADRs/document 0.85 (95% CI 0.78 to 0.93, p<0.001), and 21% fewer ADRs listed in Australia compared with the USS (ratio of ADRs/ document 0.79, 95% CI 0.74 to 0.85, p<0.001). There were no variations in ADR severity. The presence and qualitative content of boxed warnings also showed substantial diversity. Conclusions International variations exist in the presentation of safety data in drug prescribing information, which may have important implications for patient safety. Better international coordination is necessary to enhance use of this information for patient decision-making. [ABSTRACT FROM AUTHOR]