AI-Driven Human-Robot Collaboration for Autonomous Healthcare Systems in Life Science

Artificial Intelligence (AI) and robotics converging in the healthcare and life sciences will transform both the clinical and laboratory environments by responding to the increasing demands of precision and scalability. Nevertheless, conventional robotic systems tend to be fixed, with no advancement and adaptive cooperation to welcome human surveillance and autonomous functions in a safe manner. The study offers an AI-based system of human-robot collaboration (HRC) that is specifically based on autonomous healthcare systems. It is based on a methodology combining an AI decision module on reinforcement learning to find an optimal task allocation with a PID-controlled adaptive interface with the ability to correct errors in real-time. The framework was tested by simulation and experiment on a 6-DOF collaborative robotic arm. The performances prove to be quite significant: the system scored 92% in regard to the tasks success in the laboratory and 90% in the situations with patients. Moreover, the deployment enabled the cut of the human workforce on repetitive labs by 50% and the efficiency of collaboration in general by 40%. Safety is also a major agenda; the framework has a 99% safety compliance level and a high user satisfaction rating of 4.7/5. Although a small error of 3% remains because of sensor calibration factors, this result suggests that when compared to other autonomous systems, the proposed framework is capable of significantly improving scalability and safety. This study has offered a scalable basis for larger implementations in practice in clinical and laboratory settings.