With artificial intelligence (AI) becoming an omnipresent focus of innovation in the healthcare sector, it should come as no surprise that large language models鈥� (LLMs) application in robotic surgery is becoming an area of growing interest.
According to GlobalData analysis, AI in healthcare is forecast to reach a $19bn valuation by 2027. Paired with the overall global robotic surgical systems market, which is set to reach a valuation of $9.2bn by 2034, up from $2.9bn in 2024, as per a GlobalData market model, the intersection of the space鈥檚 present a large value proposition.
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For robotic surgery, LLMs, a form of AI trained on datasets to perform a stated function, are having a strong impact on how robotic surgery is planned, executed, and taught. Specifically, LLMs hold potential in supporting surgeons in making and in accelerating skill acquisition.
Research indicates that the technology not only presents opportunities for integration towards improving existing surgical robotic processes, but in companies鈥� robotic surgical system development pathways.
However, as LLMs鈥� use rises, a potential issue with their integration into medtech areas such as robotics, is the heterogeneity of use cases, says Erez Kamanski, CEO of AI compliance company Ketryx.
鈥淚n order to prove that something works, you need to prove it for a specific thing that it does. And so companies [using LLMs in robotic surgery] would need to create a lot of different use cases for the products, and then run evaluations and tests that prove, for a wide percentage of those designated situations, that it does work.鈥�
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By GlobalDataKaminski also highlights that medical device regulations require a system to have a specific, intended task.
鈥淲hether that’s an LLM or convolutional neural network, these technologies are just ways to perform a task. In robotic surgery, companies will first need to take a step back and specifically define what function an LLM is performing,鈥� he says.
Helping surgeons in the robotic surgery ecosystem
A key application of LLMs in robotic surgery to date has been in aiding clinicians in gain enhanced about the surgical environment, such as a tools’ location during surgery or its proximity to a given organ.
According to Dustin Vaughan, vice president of R&D in robotics at Asensus Surgical, a company that has developed LLMs and Large Multimodal Models (LMMs) for surgeons, its models analyse imaging and textual data to gain an understanding of the surgical environment.
鈥淭his capability has the potential to enhance a surgeon’s decision-making with valuable information exactly when it’s needed,鈥� Vaughan says.
Another primary focus of LLMs for the company is in deploying them within their development support teams 鈥� thereby accelerating both software development and production processes for Asensus鈥檚 surgical robotic systems.
Vaughan says that since Asensus has implemented tools such as GitHub Copilot and Cursor into its software development process, it has realised 鈥渟ignificant progress鈥� in accelerating code generation across different workstreams.
鈥淔or example, we have deployed a number of unit test agents within our build pipeline that have been shown to improve dry-run test efforts and deliver excellent code coverage for our comprehensive test suite.鈥�
Looking ahead, Asensus foresees additional opportunities to expand the use of LLMs to optimise workflows, enhance productivity, and support the company鈥檚 overall aims towards advancing digital surgery.
Not solely relying on LLMs
A current and significant limitation of LLMs relates to their tendency to generate inaccurate or nonfactual content 鈥� a matter that may hamstring their potential. Commonly known as 鈥榟allucinations鈥�, the issue poses a serious concern in clinical contexts, as it can and lead to potentially harmful medical decisions.
Vaughan stresses that while they continue to improve, the possibility of errors cannot be ignored, and that in a safety-critical space like surgical technology, such non-deterministic behaviour means 鈥渨e are not yet in a place where we can solely rely on AI tools and LLMs alone鈥�.
鈥淔or that reason, our development processes still require full code review for any AI-assisted code generation, ensuring safety and accuracy remain paramount,鈥� Vaughan says.
To further mitigate the threat of hallucinations, Asensus has a dedicated team to continuously research and test new platforms to ensure they are safe, effective, and aligned with its organisational needs.
鈥淭his strategy allows us to create robust, accurate solutions that adhere to our industry’s strict guidelines,鈥� says Motti Frimer, vice president R&D, digital solutions, and managing director of Asensus Surgical Israel.
鈥淲e also integrate these models into our internal processes to improve efficiency and accelerate the delivery of innovative, reliable software features,鈥� Frimer adds.
Reflecting on further challenges around LLMs, Vaughan highlights the need to learn when not to use them.
鈥淎t Asensus, we emphasise a balanced approach and use LLMs to accelerate and enhance workflows, while ensuring human oversight and rigorous validation guide every step.鈥�
The data differential and the future of LLMs in robotic surgery
While CMR Surgical does not currently employ LLMs in its robotic surgery protocols, conversations around doing so are 鈥渃urrently very active鈥�, according to the company鈥檚 chief technology officer, Chris Fryer.
The Cambridge, UK-based company was conceived of as a digital first company from the outset. Therefore, from its very first surgeries, the company has, with patient permission, captured detailed data from anonymised surgical videos.
CMR also has a registry, which is where it captures what Fryer describes as the 鈥渋ncoming factors鈥� and subsequent outcomes for patients who have undergone surgery with the company鈥檚 Versius surgical robot.
鈥淭aking this model, you’ve got an incredibly rich volume of visual data, which some big players are particularly interested in helping us to interpret,鈥� says Fryer.
Fryer notes that LLMs are increasingly being used to add value to the overall surgical experience.
鈥淭aking a cancer treatment analogy, let鈥檚 say, I’m in an abdomen, there’s fat, there’s tissue, there’s blood. A model could help me understand where exactly an organ is. This is just one example of how machine learning can augment the visual surgical process, if trained to understand and interpret the human anatomy.鈥�
CMR is also having discussions around LLMs鈥� use throughout the entire digital surgery pathway for patients.
鈥淔or instance, we are considering how LLMs can be applied to inform decisions around when to operate and when not to and, once an operation concludes, what cohort analysis can reveal about the success factors of that operation or areas that may need improving for next time.鈥�
The company is also considering LLMs use in specific use cases within robotic surgery, with an emphasis on giving surgeons 鈥渞icher, more context specific鈥� information around the surgery to help them perform it in a faster, more efficient way.
With respect to the advancement of these tools, Fryer鈥檚 view is that their development will hinge on what regulators have to say about their use cases.
鈥淎 lot of the future is going to depend on how the regulators view these models and the level of control you have to have over them,鈥� he says.
According to Fryer, one of the decisive factors is going to be for the robotic surgical sector to devise an agreed interpretation of how LLMs can be 鈥渃onstrained, but not overly so鈥�, in order to maximise their value while also ensuring they are being safely applied.
The 鈥榚xplainability鈥� of LLMs will also be a decisive factor moving forward.
Fryer concludes: 鈥淭he US Food and Drug Administration (FDA) is very clear: you can’t just treat this technology like a black box.鈥�
