Stanford-Princeton Team Launches MedOS, an AI-Robotics System to Assist Clinicians and Reduce Medical Errors

The Stanford-Princeton AI Coscientist Team announced the launch of MedOS, the first AI-XR-Cobot system designed to actively assist clinicians inside real clinical environments. Created by an interdisciplinary team led by Drs. Le Cong, Mengdi Wang, and Zhenan Bao, with clinical collaborators Drs. Rebecca Rojansky and Christina Curtis, MedOS combines smart glasses, robotic arms, and multi-agent AI to form a real-time co-pilot for doctors and nurses. Its mission is to reduce medical errors, accelerate precision care, and support overburdened clinical teams.

Physician burnout has reached crisis levels, with over 60% of doctors in the United States reporting symptoms. MedOS, detailed at ai4med.stanford.edu, is designed to alleviate this by reducing cognitive overload, catching errors, and extending precision through intelligent automation and robotic assistance. Built on innovation from the team’s previous breakthrough, the LabOS (ai4lab.stanford.edu), MedOS bridges digital diagnostics with physical action. From operating rooms to bedside diagnostics, the system perceives the world in 3D, reasons through medical scenarios, and acts in coordination with care teams.

MedOS introduces a ‘World Model for Medicine’ that combines perception, intervention, and simulation into a continuous feedback loop. Using smart glasses and robotic arms, it can understand complex clinical scenes, plan procedures, and execute them in collaboration with clinicians. The platform has shown early promise in tasks such as laparoscopic assistance, anatomical mapping, and treatment planning. In surgical simulations, it has demonstrated the ability to interpret real-time video from smart glasses, identify anatomical structures, and assist with robotic tool alignment.

Breakthrough capabilities include a multi-agent AI architecture that mirrors clinical reasoning logic, synthesizes evidence, and manages procedures in real time. MedOS achieved 97% accuracy on MedQA (USMLE) and 94% on GPQA, outperforming frontier AI models. It also leverages MedSuperVision, the largest open-source medical video dataset, featuring more than 85,000 minutes of surgical footage from 1,882 clinical experts. The system has demonstrated success in helping nurses and medical students reach physician-level performance, reducing human error in fatigue-prone environments.

Case studies include uncovering immune side effects of the GLP-1 agonist Semaglutide (Wegovy) from the FDA database and identifying prognostic implications of driver gene co-mutations on cancer patients’ survival. MedOS is launching with support from NVIDIA, AI4Science, and Nebius, and has been deployed in early pilots. Dr. Le Cong stated, ‘The goal is not to replace doctors. It is to amplify their intelligence, extend their abilities, and reduce the risks posed by fatigue, oversight, or complexity.’

MedOS will be showcased at a Stanford event in early March, followed by a public unveiling at the NVIDIA GTC conference in March 2026. Session information is available online at https://www.nvidia.com/gtc/session-catalog/sessions/gtc26-s81748/. For more information, visit the project page at https://medos-ai.github.io/ or the official site at https://ai4medos.com/.

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