Learn how AI is redefining neurosurgery. Patrick Grover from Queen Square shares insights on imaging, treatment, and clinical AI use on the FMAI podcast.
Neurosurgery is one of the most demanding fields in medicine, requiring not only intricate diagnostic expertise but also swift, highly precise treatment. As the volume of clinical data and advanced technologies such as Gamma Knife surgery continue to expand, the way we interpret and apply this information is undergoing a significant transformation. Historically, this interpretation relied almost entirely on human judgement — but that is changing.
Artificial intelligence (AI) and robotic systems are now beginning to reshape neurosurgical practice. While the high-stakes nature of the discipline initially slowed AI adoption, recent years have seen remarkable progress, led by specialists who understand both the technological and clinical complexities involved.
At the forefront of this transformation is Mr Patrick Grover, Consultant Neurosurgeon and Clinical Director at the National Hospital for Neurology and Neurosurgery (NHNN) and the Queen Square Radiosurgery Centre. Specialising in skull-base and vascular neurosurgery, he also leads research into how AI technologies can be used to improve surgical outcomes, operative training, and decision-making pathways.
In a recent podcast interview with Future Medicine AI (FMAI), Mr Grover explored how artificial intelligence is transitioning from theoretical promise to practical application in clinical neurosurgery. The conversation highlights three major areas where AI is making a real impact:
One major challenge in modern medicine is the sheer volume of unstructured clinical documentation. Mr Grover’s team is applying natural language processing (NLP) to extract meaningful insights from anonymised patient notes. These tools can reveal hidden patterns in clinical histories, helping to refine risk assessments, guide diagnosis, and personalise treatment pathways.
Importantly, Mr Grover emphasises the ongoing challenge of accessing well-annotated datasets – a prerequisite for effective training of AI models – while ensuring that patient confidentiality is preserved at all stages.
Another exciting area of development is the use of computer vision in operative video analysis. By teaching AI systems to interpret surgical footage, Mr Grover and his colleagues aim to provide real-time procedural support and post-operative feedback. This not only enhances surgical precision but also offers valuable learning tools for neurosurgical training programmes.
Annotated videos can help identify crucial steps in surgery, benchmark performance, and reduce complications – such as facial nerve injury or cerebrospinal fluid leaks – which are risks in delicate cranial procedures.
AI is revolutionising how imaging informs neurosurgical treatment. At Queen Square, Mr Grover and his team are using AI to map tumour contours, distinguish tissue types, and guide treatment planning with greater precision.
“We developed an algorithm that can automatically contour benign brain tumours on scans,” explains Mr Grover. “Next, we’re applying it to more complex shapes, like post-operative residuals, and using radiomics to identify tumour features linked to growth or treatment response – helping us build predictive models to guide decision-making.”
By combining imaging data with AI-driven analysis, clinicians can create more accurate, personalised treatment plans – including when and how to use Gamma Knife radiosurgery.
Mr Grover explains that despite the great advancements of AI, it is still essential to maintain a level of clinical oversight. He also emphasises the importance of collaboration, so that worldwide medical centres are sharing research findings and a consistent approach is maintained, and findings can be successfully integrated into current clinical practice. However, he acknowledges that even experts can disagree, and data is often unstructured.
While AI can provide more measured and objective insights, there are still some limitations when it comes to applying technology to highly complex medical procedures. It is crucial that clinicians always understand the reasoning behind AI driven directions so they can be validated, and a high level of transparency is maintained in neurosurgical practice.
AI is no longer limited to academic labs or future-facing white papers. It is now being shaped into functional tools that support safer surgeries, better patient outcomes, and more efficient training systems. For clinicians like Mr Grover, the goal is not to replace surgical expertise but to enhance it with intelligent, accountable systems that work alongside human decision-makers.
Contact the Queen Square team for a consultation to learn more about personalised treatment pathways today.
Centres of Excellence for Stereotactic Radiosurgery treatment of complex Brain Tumours
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