Explainability Subcard
intended_domain
Surgical policy online evaluation and synthetic data generation.
Model Type
Diffusion Transformer
Intended Users
Medical Robotics Engineers, Surgeons
Output
Types: A sequence of 12 video frames. Formats: Red, Green, Blue (RGB)
Describe how the model works:
The model accepts a 28-dimensional action vector (14 dimensions per arm) alongside the current video frame, and predicts the subsequent 12 frames. Through autoregressive rollout, it can generate videos of complete surgical trajectories from either learned policies or manually designed action sequences.
Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:
None
Technical Limitations & Mitigation:
The model may underperform in poor or variable lighting conditions, occlusions from instruments or blood, and specular reflections, which can degrade visual predictions. The model may not perform well for out-of-distribution scenarios including novel procedures, unusual anatomies, or emergency situations not well-represented in training data; rapid motions or long-horizon predictions where autoregressive drift accumulates errors; actions beyond the trained kinematic range or near joint limits; and generalization challenges across different camera placements, surgical sites, or surgeon styles.
Mitigation: To mitigate these limitations, we recommend performing data augmentation with lighting and occlusion variations; uncertainty estimation and out-of-distribution detection to flag anomalous states; limiting autoregressive rollout length with periodic ground-truth re-initialization; enforcing kinematic and safety constraints; multi-site training data collection; and maintaining strict human oversight with multiple safety layers.
Verified to have met prescribed NVIDIA quality standards:
Yes
Performance Metrics:
Robust L1 and SSIM vs. number of generated frames.
Potential Known Risks:
The model may generate videos that contain artifacts. The model may inaccurately represent 3D space, 4D space-time, or physical laws in the generated videos, leading to artifacts such as disappearing or morphing objects, unrealistic interactions, implausible motions, and physically inconsistent outcomes.
Licensing:
Governing Terms: Use of this model is governed by the NVIDIA Open Model License Agreement.