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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]

Artificial intelligence (AI) has the potential to revolutionize the field of surgery by improving diagnostic accuracy, workflow efficiency, and reducing costs. Large language models (LLMs), such as ChatGPT, have gained attention for their ability to mimic human conversation and respond intelligently to user prompts. LLMs like ChatGPT can streamline tasks such as symptom assessment, diagnostic decision support, and patient education. They also have potential applications in surgical education and research. However, there are concerns about the perpetuation of racism and inequity within AI systems, as well as data privacy and sovereignty issues. The surgical community must ensure the fair and responsible implementation of AI tools. [Extracted from the article]

Understanding antimicrobial usage patterns and encouraging appropriate antimicrobial usage is a critical component of antimicrobial stewardship. Studies using VetCompass Australia and Natural Language Processing (NLP) have demonstrated antimicrobial usage patterns in companion animal practices across Australia. Doing so has highlighted the many obstacles and barriers to the task of converting raw clinical notes into a format that can be readily queried and analysed. We developed NLP systems using rules‐based algorithms and machine learning to automate the extraction of data describing the key elements to assess appropriate antimicrobial use. These included the clinical indication, antimicrobial agent selection, dose and duration of therapy. Our methods were applied to over 4.4 million companion animal clinical records across Australia on all consultations with antimicrobial use to help us understand what antibiotics are being given and why on a population level. Of these, approximately only 40% recorded the reason why antimicrobials were prescribed, along with the dose and duration of treatment. NLP and deep learning might be able to overcome the difficulties of harvesting free text data from clinical records, but when the essential data are not recorded in the clinical records, then, this becomes an insurmountable obstacle. [ABSTRACT FROM AUTHOR]

Automated information extraction might be able to assist with the collection of stroke key performance indicators (KPI). The feasibility of using natural language processing for classification‐based KPI and datetime field extraction was assessed. Using free‐text discharge summaries, random forest models achieved high levels of performance in classification tasks (area under the receiver operator curve 0.95–1.00). The datetime field extraction method was successful in 29 of 43 (67.4%) cases. Further studies are indicated. [ABSTRACT FROM AUTHOR]

Background: Currently there is an incomplete understanding of antimicrobial usage patterns in veterinary clinics in Australia, but such knowledge is critical for the successful implementation and monitoring of antimicrobial stewardship programs. Methods: VetCompass Australia collects medical records from 181 clinics in Australia (as of May 2018). These records contain detailed information from individual consultations regarding the medications dispensed. One unique aspect of VetCompass Australia is its focus on applying natural language processing (NLP) and machine learning techniques to analyse the records, similar to efforts conducted in other medical studies. Results: The free text fields of 4,394,493 veterinary consultation records of dogs and cats between 2013 and 2018 were collated by VetCompass Australia and NLP techniques applied to enable the querying of the antimicrobial usage within these consultations. Conclusion: The NLP algorithms developed matched antimicrobial in clinical records with 96.7% accuracy and an F1 Score of 0.85, as evaluated relative to expert annotations. This dataset can be readily queried to demonstrate the antimicrobial usage patterns of companion animal practices throughout Australia. [ABSTRACT FROM AUTHOR]