Use of a large language model (LLM) for ambulance dispatch and triage.

Introduction: Large language models (LLMs) have grown in popularity in recent months and have demonstrated advanced clinical reasoning ability. Given the need to prioritize the sickest patients requesting emergency medical services (EMS), we attempted to identify if an LLM could accurately triage ambulance requests using real-world data from a major metropolitan area.

Methods: An LLM (ChatGPT 4o Mini, Open AI, San Francisco, CA, USA) with no prior task-specific training was given real ambulance requests from a major metropolitan city in the United States.

Recurrent Nasal Subcutaneous Emphysema: A Rare Complication of Open Septorhinoplasty.

Subcutaneous emphysema (SE) is a notably rare complication of surgical procedures, particularly in the context of rhinoplasty. In this report, we present a novel case of recurrent nasal SE following routine open septorhinoplasty in a 59-year-old male. This patient developed persistent subcutaneous air over the nasal dorsum, linked to a fistula tract to the nasal vault, despite initial conservative management with needle aspiration.

Disagreements in Medical Ethics Question Answering Between Large Language Models and Physicians.

Importance: Medical ethics is inherently complex, shaped by a broad spectrum of opinions, experiences, and cultural perspectives. The integration of large language models (LLMs) in healthcare is new and requires an understanding of their consistent adherence to ethical standards.

Objective: To compare the agreement rates in answering questions based on ethically ambiguous situations between three frontier LLMs (GPT-4, Gemini-pro-1.