BID Artifacts: An Artificial Intelligence–powered Briefing–Intraoperative–Debriefing Platform for Competency-based Plastic Surgery Education
David Fernando Duque-Ropero, Andres Fernando Gómez-SamperBackground:
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.