Recent Advances in Neuroradiology

by Dr Robin Joseph, Consultant Neuroradiologist, Oxford University Hospitals

Neuroradiology is a fascinating field of medicine that has transformed beyond recognition since its infancy more than 50 years ago. Advances in MRI, CT and Interventional Neuroradiology have made great strides in the diagnosis and treatment of neurological conditions. Within this article, I will focus on some recent advances and how they are changing this exciting speciality.

Artificial Intelligence in Neuroradiology

Artificial intelligence (AI) in neuroradiology is already making a practical difference, but it is important to distinguish between different categories of AI. Large Language Models (LLMs) such as ChatGPT are generative systems designed to produce text. In contrast, most deployed clinical neuroradiology AI today is computer vision (image analysis) used for tasks such as detection, triage, segmentation and quantitative measurement on CT and MRI. These tools are gradually becoming integrated into clinical workflows to support time-critical decision-making rather than to “write” the diagnosis.

LLMs may eventually help with administrative tasks, for example, drafting non-clinical summaries or supporting structured documentation, but using generative text to produce clinical radiology reports raises well-recognised safety concerns. A key limitation is the risk of “hallucinations”, where AI-generated text can include plausible but factually incorrect statements. For clinical reporting, any future use of LLMs would require rigorous validation, appropriate governance frameworks, and a clear requirement that clinicians retain responsibility for the final interpretation and sign-off. This principle of “human in the loop” remains non-negotiable in the current regulatory environment.

AI is also transforming MRI acquisition itself. Leading scanner manufacturers, including GE HealthCare (AIR Recon DL), Philips (SmartSpeed) and Siemens Healthineers (Deep Resolve), have developed deep learning-based image reconstruction platforms that significantly reduce scan acquisition times without sacrificing image quality. In neuroradiology, this has practical benefits across multiple clinical scenarios: faster brain tumour follow-up protocols, shorter epilepsy sequences, and particularly valuable reduced scan times in paediatric patients where minimising general anaesthetic and sedation exposure is a clinical priority. The ability to scan larger anatomical volumes in shorter acquisition windows also supports high-throughput teleradiology services, where efficiency and image consistency across sites are operationally significant.

Computer-vision AI can support neuroradiology workflow by automatically flagging suspected critical findings, prioritising worklists, and generating quantitative outputs, for example, highlighting suspected intracranial haemorrhage, large vessel occlusion, or producing segmentation-based measurements. The aim is to reduce time-to-diagnosis for urgent cases and support consistent reporting. As with any clinical decision-support tool, performance depends on local implementation, imaging protocols and ongoing post-deployment monitoring, and it remains essential that radiologists understand the tool’s intended use, validated performance parameters, and limitations.

Beyond the acute pathway, there are established neuroradiology AI applications focused on quantitative measurement and segmentation. Brain volumetry tools are increasingly used to support dementia pathways, providing objective measures of hippocampal and cortical volume that complement clinical assessment. Automated lesion quantification tools in multiple sclerosis allow consistent tracking of disease burden and new lesion accrual over time, supporting MDT decision-making around disease-modifying therapy. Tumour segmentation tools can assist with measurement at diagnosis and follow-up comparison in line with RANO criteria. Used appropriately, these systems improve consistency and reduce inter-observer variability, but at the present moment, these are used sparingly and have not yet provided the real-world benefit they have promised. They require ongoing clinical oversight and correlation with the patient’s full clinical picture.

It is important to note that AI tools used in clinical practice in the UK must meet regulatory requirements, including UKCA marking under the Medical Devices Regulations and, where applicable, alignment with NICE evidence standards for digital health technologies. Procurement and governance teams should expect to see documented clinical validation data, post-market surveillance plans, and clear delineation of intended use, particularly as the MHRA continues to develop its Software as a Medical Device (SaMD) framework. Across the teleradiology sector, where AI tools are deployed at scale across multiple sites and reporting environments, robust governance infrastructure is essential to ensure consistent, safe performance.

Looking ahead, many teams hope that AI will continue to reduce variation and support faster, more consistent decision-making in neuroradiology. In practice, today’s deployed tools are starting to assist clinicians by providing rapid image-based alerts and quantitative measurements rather than fully automating interpretation and reporting. As these technologies mature, the questions facing the field are no longer solely about whether AI works, but about how it is governed, monitored and integrated into clinical accountability structures. We should embrace evidence-based innovation while remaining realistic about limitations and ensuring robust oversight at every level.

Interventional Neuroradiology

Interventional Neuroradiology (INR) has changed beyond recognition in the last 20 years. When I started neuroradiology training, the majority of consultants who undertook INR procedures did these as a “side hustle”. As the procedures became more complex and more could be achieved, more and more consultants devoted time to this exciting and demanding field. With the serendipitous discovery of clot retrieval with stents and thrombo-aspiration, INR has become a key part of the acute stroke pathway. The efficacy of this procedure has driven big changes in neuroradiology departments across the UK. Within our own department, we have moved to 24/7 service provision of thrombectomy, offering this since January 2024. This change has been reflected in the UK generally and has led to a big expansion in INR consultant numbers.

It is worth noting that AI is already embedded in the acute stroke pathways that make thrombectomy possible at pace and scale. Tools such as Brainomix e-Stroke, RapidAI and Viz.ai are used across UK stroke networks to support rapid CT and CTA interpretation, providing automated ASPECTS scoring, perfusion processing, and large vessel occlusion detection with direct clinician notification. In time-critical stroke care, where minutes translate directly into neurological outcomes, these workflow tools represent an example of nationally deployed AI in neuroradiology.

However, round-the-clock provision of thrombectomy is not universal, with 7 out of 24 England centres not yet able to provide this 24/7 service as of April 2026. Interventional Neuroradiology requires highly trained individuals working as part of a larger team, including neuro-anaesthetists, specialised nursing and radiographer staff, as well as close liaison with stroke physicians and their teams. This is clearly complex and time-consuming to set up and goes some way to explain the disparity across the UK. With time, these differences should become less pronounced.

With the expansion of thrombectomy, this has meant an inevitable reduction in diagnostic work undertaken by INR consultants. Neuroradiology departments will need to increase diagnostic consultant numbers substantially to ensure that diagnostic reporting and MDT support remain at current levels. This workforce challenge is one that the speciality and the broader NHS radiology community will need to plan for proactively in the years ahead.

Summary

Recent advances in neuroradiology are making a tangible difference to the diagnosis and management of neurological conditions. Thrombectomy has transformed the acute stroke pathway, and its continued rollout, supported by AI-driven triage and workflow tools, will improve outcomes for more patients. Computer-vision AI for detection, triage, quantitative measurement and image reconstruction is becoming increasingly embedded in neuroradiology practice, with a regulatory and governance framework that is still maturing alongside it. The most important task for the speciality now is not simply to adopt these technologies, but to deploy them with great care and thought, appropriate validation, oversight and accountability, so that their benefits are realised safely and consistently across all the environments in which neuroradiology is practised.

 

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