Inside the Operating Room: Humans, Robots, and AI

Surgery is a Team Effort—and the Team is Changing

Have you ever had surgery, or seen an operation on television? Surgery helps doctors fix many health problems, from removing an infected appendix to repairing broken bones, removing tumors, or replacing damaged organs. Around the world, millions of people need surgery every year [1].

If you have seen a surgery, you probably noticed that the operating room is a busy place. Monitors beep, lights are bright, and the surgical team buzzes about doing their jobs. While the surgeon focuses on the operation itself, nurses prepare the patient and hand over instruments, an anesthetist makes sure the patient is “asleep” and carefully watches their vital signs, and other team members keep the space clean, organized, and functioning smoothly. Everyone has a specific role, and teamwork is just as important as the surgeon’s skills.

Surgery is one of the most challenging aspects of medicine. The human body is complex, and every patient—and therefore every operation—is unique. Surgical teams must often make decisions very quickly or with limited information, and even tiny missed signals or minor communication breakdowns can quickly turn serious. Problems during or after an operation are one of the leading causes of death worldwide [2]. Medical researchers are always looking for better ways to make operations easier for surgical teams and safer for patients.

Two kinds of technology are starting to help surgical teams work more safely, precisely, and efficiently: surgical robots and artificial intelligence (AI). As you will learn, these tools are not meant to replace doctors or nurses—however, over time, they may change how surgical teams work.

Surgical Robots: Machines That Help With Operations

Surgical robots are machines designed to help with specific physical tasks during an operation [3]. Some surgical robots can hold cameras that provide a clear, magnified view inside the body. Others move instruments, such as scissors or graspers, with steady, precise motions that can be difficult for human hands to repeat over long periods of time. Surgical robots are already used in certain kinds of surgeries where surgeons must work in tight spaces while protecting nearby nerves and organs. They are also used for certain abdominal surgeries in which surgeons work through very small openings rather than making large cuts (Figure 1A). Generally, the surgeon sits nearby and controls the robot’s movements, often while watching the operation on a screen (Figure 1B). Researchers are also beginning to explore robots that can help with other tasks in the operating room, such as passing instruments or helping keep the surgical area clean (Figure 2).

Figure 1 - A surgical robot at work. (A) Some surgical robots can work through very small openings instead of making large cuts, which can make it easier for the patient to recover. (B) Typically, the surgeon sits at a screen nearby and controls the robot’s movements remotely.

Figure 2 - In addition to performing surgeries, current trends in technology indicate that robots might be able to help the surgical team with other operating room tasks, such as positioning the patient, selecting and handing over instruments, performing CPR or other emergency responses if necessary, taking images of the patient (such as x-rays), cleaning floors or equipment, delivering supplies, and controlling the environment in the room.

Surgical robots are powerful tools that can extend what human hands can do, making operations more controlled and less tiring for the surgical team. However, they have their limits. For example, robots do not make decisions like when or where to cut [4]. Those choices are always made by trained medical experts, and humans always remain responsible for the operation. Because surgical robots are complex medical devices that keep evolving, researchers evaluate them step by step and monitor how well they perform over time [5].

Artificial Intelligence: Computer Systems That Analyze and Assist

AI is a newer addition to the operating room. While it is not yet a central part of most operations, researchers are excited about how it might support surgical teams in the future [6, 7].

Unlike robots, AI is not a physical machine that holds instruments or touches the patient. Instead, it is software that runs on computers connected to operating room equipment. AI systems can collect lots of information from cameras, microphones, patient monitors, and sensors on surgical tools, and even combine operating room data with a patient’s medical history (Figure 3). No single person could easily watch, listen to, and interpret all of this information at once, but AI systems can organize it and look for important patterns.

Figure 3 - AI systems consist of software that runs on computers. When connected to operating room equipment, surgical AI helpers can act like an extra set of eyes and ears, collecting more data than any human could ever keep track of. AI looks for patterns in all of this information and can then communicate with the surgical team to keep them on track and warn them if something unusual is happening.

For example, an AI system might recognize which step of a surgery is happening and share that info with the team, helping everyone stay coordinated—especially during long operations when team members may change or take breaks. AI systems might also watch how a surgery is unfolding. For instance, AI might notice when a step takes longer than expected or when a patient’s vital signs change, and alert the team so they can take a closer look to see if there is a problem. Basically, AI acts like an extra set of eyes and ears, quietly keeping track of the big picture and pointing out changes that team members might miss if they are tired or too busy.

Like surgical robots, the goal is not for AI to “take over” an operation. It acts more like a surgical “copilot” that monitors what is happening, while the “pilot”—the surgeon—remains in charge.

Training AI for the Operating Room

Before doctors and nurses are allowed to work in an operating room, they go through years of training: studying anatomy, practicing procedures, and learning how to respond when things do not go as planned. In a different way, AI systems also need training before they can be used during surgeries.

Training an AI system means giving it many examples of past operations to learn from. These examples can include videos showing how past surgeries unfolded, information about the condition of patients before surgery, records of what happened during operations (such as how long various steps took or how the patient’s vital signs changed), and notes about what happened afterward, including how well patients recovered. By “studying” hundreds of past surgeries, AI systems learn to recognize patterns linked with smooth operations and quick recoveries, versus patterns that appear more often in surgeries that lead to complications.

Where the training examples come from matters. If most of the examples come from certain kinds of hospitals, from wealthier countries, or from only certain types of patients (such as mostly adults or mostly men), the AI may work best in those situations but not as well in others [8]. For example, an AI system trained primarily on surgeries from large hospitals with advanced equipment might struggle when used in smaller hospitals that have different tools or fewer resources. Collecting large amounts of high-quality training data from many different hospitals and patient groups can be difficult, and researchers are working on better ways for hospitals to safely share and combine data.

Even if AI systems are well trained, researchers must spend a lot of time testing them to make sure they work safely and reliably. They need to check how well the systems perform across different hospitals, procedures, and patient groups, and look closely at where the systems fall short and where they need improvement.

Will Machines Take Over Surgeries?!

At this point, you might be wondering whether robots and AI will eventually replace human surgeons altogether. The short answer is no—not anytime soon.

Even when robots and AI systems work exactly as designed, they cannot run a surgery on their own. Surgery rarely follows a perfectly fixed plan, and unexpected events can happen quickly. As we explained, machines respond only to the situations they have been prepared to handle, which means they can make mistakes when situations change or when something unusual happens [9]. They cannot yet step back, rethink goals, or adapt creatively to a brand-new problem. Only human experts can do that.

Although robots and AI will not replace surgical teams, they will change how those teams work together. Surgeons will spend less time physically holding tools and more time watching over how the surgery is progressing, making decisions, and guiding the rest of the team. If robots handle routine tasks like handing tools to the surgeon or keeping the surgical area clean, other team members will also be able to focus more on overall patient care.

As these technologies become more common, surgical teams will need new kinds of training. Team members will have to learn how to work with surgical robots, understand information from AI systems, and decide when that information is helpful—and when it is not helpful or even wrong. New types of jobs will probably be created too, such as specialists who help manage surgical robots or AI systems during operations. These roles will combine medical knowledge with new technical skills.

Making Surgery Better for Everyone

Unfortunately, billions of people worldwide live in places where surgery is difficult to get or not as safe as it should be. Robots and AI will not automatically solve this problem on their own. Advanced technologies are expensive, so at first they may be available mainly in larger hospitals or wealthier parts of the world. Smaller hospitals or places with fewer resources may not have access right away. Making sure these tools eventually benefit many different patients, in many different settings, is a major challenge for healthcare systems.

Using powerful technologies in medicine also requires clear rules and open communication. Surgical teams need guidance on when and how robots or AI should be used, and patients deserve clear explanations about how these technologies are involved in their care. When patients understand what a technology can and cannot do, it helps build their trust in the medical team.

Ultimately, the future of surgery will go beyond smarter technologies. It will involve humans teaming up with technology and using these powerful tools carefully. Even when surgical robots and AI become common in the operating room, human experts will still make decisions, communicate with patients, and take responsibility for surgeries. Surgical teams of the future will make surgery safer, more precise, and better matched to each patient’s needs, supporting better health for people everywhere.

Glossary

Surgical Team: ↑ A group of trained healthcare workers who work together during an operation, including surgeons, nurses, anesthetists, and others who help keep the patient safe.

Vital Signs: ↑ Basic body measurements, such as heart rate, breathing, and blood pressure, that show how well a person’s body is working during surgery.

Surgical Robots: ↑ Machines that surgeons control to help perform certain tasks during an operation, such as holding cameras or moving tools with steady, precise motions.

Artificial Intelligence: ↑ Computer systems that can analyze large amounts of information, recognize patterns, and help people make decisions, without thinking or acting on their own.

Sensors: ↑ Devices that detect changes in things such as movement, pressure, sound, or body signals and send that information to machines or computers.

Conflict of Interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We wish to thank Dr. Susan Debad for providing us with a first draft and for her continued collaborative input as co-author. We would also like to thank the coauthors of the original manuscript: Raghav Khanna, Nikola Fischer, Nicholas Raison, Margarita Ciabattini, Harry Robertshaw, Maxence Boels, Mohsan Malik, Veronica Granados, Tom Vercauteren, Jonathan Shapey, Thomas Booth, Asit Arora, Giorgio Gandaglia, Alberto Briganti, Francesco Montorsi, Christos Bergeles, and Sebastien Ourselin. This study received funding from the Dr Recordati Surgical Data Science Programme, Innovate UK under grant agreement no. 10111748 and by the Engineering and Physical Sciences Research Council (EPSRC) [EndoTheranostics/EP/Z003172/1] under the Horizon Europe Guarantee Extension. PD was supported by EPSRC, under grant no. EP/Y009800/1, through funding from Responsible AI UK (RAI UK). He also acknowledges funding from the Trustworthy Autonomous Systems (TAS) Hub and United Kingdom Research and Innovation (UKRI). He recognizes support from the Wellcome Trust for Surgical and Interventional Engineering, the London Institute for Healthcare Engineering (LIHE), the Hinduja-King’s Academy, Alberto Recordati, the King’s-Vattikuti Institute, The Urology Foundation and King’s College London (KCL). The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

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↑Granados, A., Khanna, R., Fischer, N., Raison, N., Ciabattini, M., Robertshaw, H., et al. 2026. Evolving surgical teams in the age of artificial intelligence and robotics. Front. Sci. 4:1783803. doi: 10.3389/fsci.2026.1783803

[1] ↑ Dobson, G. P. 2020. Trauma of major surgery: a global problem that is not going away. Int. J. Surg. 81:47–54. doi: 10.1016/j.ijsu.2020.07.017

[2] ↑ Nepogodiev, D., Martin, J., Biccard, B., Makupe, A., and Bhangu, A. 2019. Global burden of postoperative death. Lancet 393:401. doi: 10.1016/S0140-6736(18)33139-8

[3] ↑ D'Ettorre, C., Mariani, A., Stilli, A., Rodriguez y Baena, F., Valdastri, P., Deguet, A., et al. 2021. “Accelerating surgical robotics research: a review of 10 years with the da Vinci research kit”, in IEEE Robotics & Automation Magazine (Vol. 28, IEEE), 56–78. doi: 10.1109/MRA.2021.3101646

[4] ↑ Attanasio, A., Scaglioni, B., De Momi, E., Fiorini, P., and Valdastri, P. 2021. Autonomy in surgical robotics. Annu. Rev. Control Robot. Autono. Syst. 4:651–79. doi: 10.1146/annurev-control-062420-090543

[5] ↑ Marcus, H. J., Ramirez, P. T., Khan, D. Z., Horsfall, H. L., Hanrahan, J. G., Williams, S. C., et al. 2024. The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring. Nat. Med. 30:61–75. doi: 10.1038/s41591-023-02732-7

[6] ↑ Maier-Hein, L., Eisenmann, M., Sarikaya, D., März, K., Collins, T., Malpani, A., et al. 2022. Surgical data science – from concepts toward clinical translation. Med. Image Anal. 76:102306. doi: 10.1016/j.media.2021.102306

[7] ↑ Vercauteren, T., Unberath, M., Padoy, N., and Navab, N. 2020. CAI4CAI: the rise of contextual artificial intelligence in computer assisted interventions. Proc. IEEE Inst. Electr. Electron Eng. 108:198–214. doi: 10.1109/JPROC.2019.2946993

[8] ↑ Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., and Tzovara, A. 2021. Addressing bias in big data and AI for health care: a call for open science. Patterns 2:100347. doi: 10.1016/j.patter.2021.100347

[9] ↑ Khanna, R., Raison, N., Granados Martinez, A., Ourselin, S., Montorsi, F., Briganti, A., et al. 2025. At the cutting edge: the potential of autonomous surgery and challenges faced. BMJ Surg. Interv. Health Technol. 7:e000338. doi: 10.1136/bmjsit-2024-000338