Medical Robotics Foundation Models

Surgical world models, Open-H-Embodiment, and computer vision for robotic training workflows.

My medical robotics work applies computer vision and generative models to surgical training, robotic manipulation, and future foundation models for healthcare robotics.

Open-H-Embodiment dataset overview

The public work spans diffusion-based suturing world models, Open-H-Embodiment contributions, surgical phase recognition, and computer-vision scoring for endoscopy training. Together, these projects show how deployed perception and generative modeling can support training, assessment, simulation, and eventually more capable medical robots.

Role: Technical project lead or contributing author across medical AI projects with Columbia and Northwell Health collaborators
Keywords: Video diffusion, surgical action modeling, foundation-model datasets, automated skill assessment, surgical workflow understanding, medical robotics

Links: Suturing GitHub, Open-H GitHub, Hugging Face model, Suturing World Models, suturing arXiv, Open-H project, Open-H arXiv.

References

2026

Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics

Nigel Nelson, Juo-Tung Chen, Jesse Haworth, Xinhao Chen, Lukas Zbinden, Dianye Huang, Mattia Ballo, Filippo Filicori, Mehmet K Turkcan, and others

Apr 2026

Abs arXiv DOI HTML Code Dataset Model

Open-H-Embodiment is a multi-institution open dataset for medical robot learning with synchronized video and kinematics across healthcare robotic platforms, enabling foundation models and world models for medical robotics.

2025

Towards Suturing World Models: Learning Predictive Models for Robotic Surgical Tasks

Mehmet Kerem Turkcan, Mattia Ballo, Filippo Filicori, and Zoran Kostic

Apr 2025

Abs arXiv DOI HTML Code Model

This work fine-tunes video diffusion models on annotated surgical suturing demonstrations to model ideal and non-ideal fine-grained robotic surgical actions.