From Multimodal Observation to Interpretable Suggestions: Counterfactual Time-Expanded Relational Modeling of Surgical Teams

TL;DR - This paper introduces a multimodal, time-expanded relational framework for modeling surgical team dynamics and generating interpretable suggestions for better teamwork. It matters because surgical AI typically emphasizes technical execution rather than the behavioral interactions that also affect patient safety.

• Represents evolving team behaviors and relationships with time-expanded graphs.
• Targets low-data surgical settings while retaining strong relational and temporal expressivity.
• Uses counterfactual analysis to identify minimal, structured behavioral or interaction changes associated with improved team performance.
• Experiments on simulated procedures show improved prediction across multiple behavioral and interaction goals while yielding insights into team dynamics.