BID Artifacts: An Artificial Intelligence-powered Briefing-Intraoperative-Debriefing Platform for Competency-based Plastic Surgery Education.

BACKGROUND: The briefing-intraoperative-bebriefing (BID) framework has shown promise in surgical education but lacks scalable digital implementation. We developed BID Artifacts, an artificial intelligence (AI)-powered web platform that automates BID using large language models and generative AI for plastic surgery training. The platform was developed entirely by a practicing plastic surgeon using AI-assisted coding, without an engineering team. METHODS: We designed a progressive web application integrating Claude Sonnet (Anthropic) for case generation and adaptive feedback, and Gemini Flash (Google) for anatomical diagram generation. Thirteen users across 5 training levels (postgraduate year-1 through fellow) completed BID sessions on diverse surgical topics. Usability was assessed using the system usability scale and educational perception via an 8-item Likert survey. RESULTS: The mean system usability scale score was 82.9 ± 14.8 (grade B, good), with 84.6% scoring 72 or above. Educational perception averaged 4.70 out of 5.0 ± 0.41. The highest-rated item was procedural preparedness (4.92/5.0). Immediate AI feedback (45%) and critical thinking development (27%) were the most valued features. The average cost per session was US $1.05 with 0 infrastructure costs. CONCLUSIONS: BID Artifacts demonstrates a dual application of AI in plastic surgery education: as a pedagogical engine automating the BID framework, and as a development tool enabling a clinician to build a complete educational platform without engineering support. Further validation with larger cohorts is warranted.