Blood suction and irrigation are among the most critical support tasks in robotic-assisted minimally invasive surgery (RMIS). Usually, suction/irrigation tools are controlled by a surgical assistant to maintain a clear view of the surgical field. Thus, the assistant’s contribution to other emergency support tasks is limited. Similarly, when the surgical assistant is not available to perform the blood suction, the leading surgeon must take over this task, which in a complex surgical procedure can result in an unnecessary increment in the cognitive load. To alleviate this problem, we have developed a semi-autonomous robotic suction assistant, which was integrated with a Da Vinci Research Kit (DVRK). At the heart of the algorithm, there is an autonomous control based on a deep learning model to segment and identify the location of blood accumulations. This system provides automatic suction allowing the leading surgeon to focus exclusively on the main task through the control of key instruments of the robot. We conducted a user study to evaluate the user’s workload demands and performance while doing a surgical task under two modalities: (1) autonomous suction action and (2) a surgeon-controlled-suction. Our results indicate that users working with the autonomous system completed the task 161 seconds faster than in the surgeon-controlled-suction modality. Furthermore, the autonomous modality led to a lower percentage of bleeding in the surgical field and workload demands on the users (p-value<0.05). These results show how leveraging state-of-the-art AI algorithms can reduce cognitive demands and enhance performance.