Force Sensing and Haptic Feedback in Robotic Surgery: A Bibliometric and Knowledge-Mapping Analysis of Technological Evolution and Translational Trajectories
This bibliometric analysis of 2,872 publications from 2010 to 2026 reveals that force sensing and haptic feedback in robotic surgery are in a rapid-growth phase led by China and the United States, having evolved from basic teleoperation concepts toward integrated, intelligent systems while highlighting the need for standardized reporting and prospective clinical validation to advance translational trajectories.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Imagine the world of surgery as a high-stakes game of video games, but instead of a controller, the surgeon is holding a scalpel. For decades, the most advanced robotic surgeons have been like players using a joystick that only shows a 3D screen but gives no "rumble" or vibration when you bump into something. They can see the tissue perfectly, but they can't feel it. If they squeeze too hard, they might crush a delicate organ; if they squeeze too soft, the tissue might slip away. This missing sense of touch is called a "sensory deficit." To fix this, engineers are building two special tools: Force Sensing, which is like giving the robot a super-sensitive skin that can measure exactly how hard it's pushing, and Haptic Feedback, which is like sending a vibration or a "push" back to the surgeon's hand so they can feel what the robot feels. The big question everyone is asking is: Can we finally give these robotic hands a sense of touch, and will it actually make surgeries safer and better for patients?
This paper is a massive "map-making" expedition into the history of this quest. Instead of testing a new robot in a lab, the authors acted like digital detectives, scanning over 2,800 scientific papers published between 2010 and July 2026. They wanted to see how fast this field is growing, who is doing the work, and how the ideas are changing over time. Think of it as looking at a forest from a helicopter to see where the trees are tallest and how the forest is spreading, rather than climbing every single tree.
The map reveals that this field is exploding. In 2010, there were only 56 papers published about this topic. By 2025, that number jumped to 393 papers in a single year. More than half of all the research ever done on this subject was published in just the last five years (2020–2025). The authors used a mathematical model to predict the future growth of this field, and it suggests we are still in the "rapid-growth" phase, with the peak of activity likely happening around 2027. However, the actual number of papers in 2025 was even higher than the model predicted, meaning the excitement is growing even faster than the math expected.
When looking at who is doing the work, the map shows a clear split. China is the "factory floor," producing the highest number of new papers and researchers. The United States, however, is the "library," holding the papers that get cited (mentioned) the most by others, indicating a deep historical influence. Interestingly, despite all this global activity, the researchers aren't working together much. Only about 12% of the papers were written by teams from different countries working together, suggesting that while everyone is building their own version of the "feeling robot," they aren't sharing blueprints as much as they could.
The story of the technology itself has changed direction. In the early days (around 2010), the focus was on "master-slave" systems, where a surgeon moves a controller and the robot copies the move, with a focus on basic two-way communication. Today, the focus has shifted to making the sensors tiny enough to fit on the robot's tools, creating "smart" instruments that can feel tissue stiffness, and using computers to guess the force even without a physical sensor (called "sensorless" estimation). The research is moving from just building the sensor to trying to integrate it into a full system that can help surgeons learn and perform better.
However, the paper is very careful not to say the job is finished. While the technology is getting better at measuring force, the authors point out that we don't yet have strong proof that this actually leads to better outcomes for patients, like faster recovery or fewer complications. Some studies suggest that haptic feedback helps surgeons use less force and be more accurate, especially for beginners, but these are mostly small tests or simulations. The paper explicitly rules out the idea that we currently have a "universal" force limit that works for every surgery; instead, it suggests that different tissues (like a lung vs. a liver) and different tasks (like cutting vs. stitching) need their own specific rules.
In short, this paper tells us that the "feeling robot" is no longer just a sci-fi dream; it's a rapidly growing reality with thousands of researchers building the pieces. But while the sensors are getting smarter and the robots are getting more "aware," the final step—proving that these feeling robots save lives in real hospitals—is still a work in progress. The future, the authors suggest, depends on standardizing how we test these tools and running big, multi-hospital studies to see if the "rumble" in the controller truly translates to a safer surgery for the patient.
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