ArticlePlastic and reconstructive surgery. Global open2026
BID Artifacts: An Artificial Intelligence-powered Briefing-Intraoperative-Debriefing Platform for Competency-based Plastic Surgery Education.
Article in Plastic and reconstructive surgery. Global open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.