The Use of Large Language Models in Postgraduate Plastic Surgery Training: A National Survey of Plastic Surgery Residents

<b>Introduction:</b> Large language models (LLMs) like ChatGPT are used by medical trainees and professionals for learning and clinical support. This study determined how Canadian plastic surgery residents utilize and perceive LLMs for their training. <b>Methods:</b> A cross-sectional survey was distributed to all Canadian, English-speaking plastic surgery trainees (<i>N</i> = 100). Descriptive statistics and conventional content analysis were used to describe quantitative and free-text responses, respectively. <b>Results:</b> A total of <i>n</i> = 36 responses were collected (36% response rate) from Canadian plastic surgery residents. Among residents, 83.3% reported using LLMs for any purpose, and 63.8% reported using the technology for plastic surgery education. The most frequently utilized LLMs include ChatGPT (83.3%), BingAI (11.1%), and Gemini (8.3%). More than half of residents reported using LLMs a minimum of once per week (50.1%). The most common applications included explaining concepts (58.3%), explaining procedures (33.3%), answering lecture questions (27.8%), and creating presentations (27.8%). Of respondents, 94.4% reported not having received education or training on the use of LLMs, and 37.1% reported concerns with the use of the technology for plastic surgery learning. The themes that emerged from the free-text responses were categorized into 3 groups: (1) advantages, including time-efficiency and summarization, (2) disadvantages, including concerns of inaccuracies, confidentiality, and over-reliance, and (3) recommendations, such as didactic teaching sessions and workshops. <b>Conclusions:</b> LLMs are commonly used by Canadian plastic surgery residents for a variety of purposes. Most residents have not been trained on the optimal use of the technology, and surgical residency programs should consider formal LLM instruction to leverage the capabilities of this tool and mitigate potential harms.