The article presents an AI co-pilot bronchoscope robot designed to address the uneven distribution of medical resources and the scarcity of experienced practitioners, which limit access to bronchoscopy services in underdeveloped areas. The system features a user-friendly, plug-and-play catheter for robot-assisted steering, enabling access to bronchi beyond the fifth generation in average adult patients. The AI-human shared control algorithm, trained using historical bronchoscopic videos and expert imitation, allows novice doctors to perform safe and skilled lung examinations, similar to experienced colleagues. Both in vitro and in vivo tests demonstrate that the system equips novice doctors with the skills to perform lung examinations as expertly as seasoned practitioners. The study offers innovative strategies to address medical resource disparities through AI assistance, potentially improving the diagnosis and management of pulmonary disorders. The system's performance is validated through simulations, in vitro experiments on human bronchial phantoms, and in vivo demonstrations on live porcine lungs, showing high precision, reduced intervention from doctors, and improved safety and efficiency. Future research will focus on broadening the system's clinical applicability and enhancing its autonomy and explainability.The article presents an AI co-pilot bronchoscope robot designed to address the uneven distribution of medical resources and the scarcity of experienced practitioners, which limit access to bronchoscopy services in underdeveloped areas. The system features a user-friendly, plug-and-play catheter for robot-assisted steering, enabling access to bronchi beyond the fifth generation in average adult patients. The AI-human shared control algorithm, trained using historical bronchoscopic videos and expert imitation, allows novice doctors to perform safe and skilled lung examinations, similar to experienced colleagues. Both in vitro and in vivo tests demonstrate that the system equips novice doctors with the skills to perform lung examinations as expertly as seasoned practitioners. The study offers innovative strategies to address medical resource disparities through AI assistance, potentially improving the diagnosis and management of pulmonary disorders. The system's performance is validated through simulations, in vitro experiments on human bronchial phantoms, and in vivo demonstrations on live porcine lungs, showing high precision, reduced intervention from doctors, and improved safety and efficiency. Future research will focus on broadening the system's clinical applicability and enhancing its autonomy and explainability.