The Stanford-Princeton AI Coscientist Team has launched 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, according to recent studies. MedOS, accessible via ai4med.stanford.edu, is designed to alleviate physician burnout not by replacing clinicians, but by reducing cognitive overload, catching errors, and extending precision through intelligent automation and robotic assistance. Built on years of innovation from the team's previous breakthrough, the LabOS at 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 doctors, nurses, and care teams. It has been tested in surgical simulations, hospital workflows, and live precision diagnostics. 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 close collaboration with clinicians.
The platform has shown early promise in tasks such as laparoscopic assistance, anatomical mapping, and treatment planning. MedOS is modular by design, built to adapt across clinical settings and specialties. 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, functioning as a true clinical co-pilot. This tight integration of perception, planning, and action sets MedOS apart as an active collaborator in high-stakes procedures.
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, beating frontier AI models like Gemini-3 Pro, GPT-5.2 Thinking, and Claude 4.5 Opus. It also features MedSuperVision, the largest open-source medical video dataset, with more than 85,000 minutes of surgical footage from 1,882 clinical experts.
Demonstrated success includes helping nurses and medical students reach physician-level performance and reducing human error in fatigue-prone environments, with registered nurses improving from 49% to 77% with MedOS assistance and medical students from 72% to 91%. 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. Clinical collaborators can now request early access. Dr. Le Cong, leader of the Stanford-Princeton AI Coscientist Team and Associate Professor at Stanford University, stated that the goal is not to replace doctors but to amplify their intelligence, extend their abilities, and reduce risks posed by fatigue, oversight, or complexity. Dr. Mengdi Wang, co-leader of the collaboration, added that MedOS reflects a convergence of multi-agent reasoning, human-centered robotics, and XR interfaces aimed at a collaborative loop to help clinicians manage complexity in real time.
MedOS will be showcased at a Stanford event in early March, followed by a public unveiling at the NVIDIA GTC conference in March 2026. The GTC session information is available 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/.
