The real-world complexity of surgeries necessitates surgeons to have deep and holistic comprehension to ensure precision, safety, and effective interventions. Computational systems are required to have a similar level of comprehension within the operating room. Prior works, limited to single-task efforts like phase recognition or scene graph generation, lack scope and generalizability. In this work, we introduce ORQA, a novel OR question answering benchmark and foundational multimodal model to advance OR intelligence. By unifying all four public OR datasets into a comprehensive benchmark, we enable our approach to concurrently address a diverse range of OR challenges. The proposed multimodal large language model fuses diverse OR signals such as visual, auditory, and structured data, for a holistic modeling of the OR. Finally, we propose a novel, progressive knowledge distillation paradigm, to generate a family of models optimized for different speed and memory requirements. We show the strong performance of ORQA on our proposed benchmark, and its zero-shot generalization, paving the way for scalable, unified OR modeling and significantly advancing multimodal surgical intelligence. We will release our code and data upon acceptance.
View on arXiv@article{özsoy2025_2505.12890,
title={ ORQA: A Benchmark and Foundation Model for Holistic Operating Room Modeling },
author={ Ege Özsoy and Chantal Pellegrini and David Bani-Harouni and Kun Yuan and Matthias Keicher and Nassir Navab },
journal={arXiv preprint arXiv:2505.12890},
year={ 2025 }
}