Top 4 AI Use Cases in Neurology in 2024

The field of neurology is currently facing various challenges. One of the challenges, for instance, is the current 11% shortage of neurologists’ supply. This shortage is projected to rise to 19% by 2025. ¹ 

As AI changes the landscape of the healthcare sector, it can also help overcome current industry challenges in the field of neurology by enabling higher efficiency, accuracy, and better patient care. 

This article explores the top 4 AI use cases in the field of neurology to help healthcare institutions/facilities leverage this technology.

1. AI for Neuro-oncology

A brain tumor is one of the most commonly misdiagnosed illnesses in neuro-oncology. ² These misdiagnoses can happen due to various reasons, including wrong interpretation of symptoms and inaccurate analysis of medical reports. Misdiagnoses in the field of neuro-oncology can have dire consequences. 

AI can help improve the diagnosis and detection of brain tumors and other neurological cancers with high accuracy and consistency. Studies ³ show that optical imaging and deep convolutional neural networks (CNNs) can be used to accurately predict brain tumors in less than 150 seconds. 

Watch how AI is improving brain tumor diagnosis

2. AI for neuro-vascular diseases

AI has various applications for neurovascular disorders that can affect the blood supply in the brain or spinal cord. For instance, AI-enabled CT scanning for hemorrhagic patients allows automation of lesion segmentation and detection of hemorrhagic expansion.

One of the most common and dangerous neurovascular conditions is a stroke, globally ranking as the 2nd leading cause of death. ⁴ Apart from the fatal outcomes, strokes can cause other serious complications for patients, including body paralysis. 

AI can help treat patients who are affected by strokes by providing personalized treatments. See how BrainQ and Google are working on an AI-enabled medical device that can be helped to treat stroke patients:

3. AI for traumatic brain injuries

Traumatic brain injuries or TBIs are injuries that happen to the brain due to an accident. TBI affects around 60 million people every year globally, with short and long-term health consequences being in terms of their severity.

TBI detection

According to a recent study ⁵ by the University of Cambridge and Imperial College London, AI can be used to successfully and accurately scan medical images and detect different types of brain injuries.

Reducing overuse of medical imaging 

Overuse of medical imaging is another problem in pediatric TBI. Even when a mild injury happens, precautionary CT scans are performed on the child to understand the severity of the damage, and only 10% of these scans end up finding TBI. ⁶

In other words, 90% of the time, the child is needlessly exposed to radiations caused by medical imaging, with the latter producing nothing of significance. 

According to a recent study ⁷, deep neural networks can predict the need for CT scans in mild pediatric TBI, thereby reducing the overuse of medical imaging and unnecessary exposure to radiation.

4. AI for neurosurgery

Neurosurgery or brain surgery is one of the riskiest and most complicated medical procedures in the field of medicine. Even the most experienced surgeons can make various types of errors and mistakes that can have serious consequences and lead to future health issues for the patient.

AI can help improve pre-operative, intra-operative, and postoperative phases of brain surgery: 

• In preoperative surgery, AI can help improve the accuracy of diagnoses and create a better surgical plan based on historical data.
• In the intra-operative phase, AI can enable fast and accurate analysis of brain tissue during the procedure. 
• In the postoperative phase, AI can help predict postoperative complications to improve patient recovery and aftercare.

To learn more about how AI is improving the field of surgery, check out this quick read.

Further reading

• Top 18 Healthcare AI Use Cases
• 4 Ways AI is Revolutionizing The Radiology Sector
• Top 3 AI Applications in the Field of Dermatology

If you have any questions, feel free to contact us:

External Links

• 1. Schneider, Mary Ellen (2013). “Neurology shortfall to worsen by 2025.” MDedge. Revisited January 20, 2023.
• 2. “Brain tumors.” Paul & Perkins. Revisited January 20, 2023.
• 3. Hollon, T. C., Pandian, B., Adapa, A. R., Urias, E., Save, A. V., Khalsa, S. S. S., … & Orringer, D. A. (2020). “Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.” Nature medicine, 26(1), 52-58.
• 4. Donkor, Eric. (2018). “Stroke in the 21^st Century: A Snapshot of the Burden, Epidemiology, and Quality of Life.” NCBI. Revisited January 20, 2023.
• 5. Miguel Monteiro et al (2020). ‘Multi-class semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study.’ The Lancet Digital Health. Revisited January 20, 2023.
• 6. Me, H. E., Chandra, S. S., & Nasrallah, F. A. (2022). “Deep Neural Networks Predict the Need for CT in Pediatric Mild Traumatic Brain Injury: A Corroboration of the PECARN Rule.” Journal of the American College of Radiology: JACR, S1546-1440. Revisited January 20, 2023.
• 7. Me, H. E., Chandra, S. S., & Nasrallah, F. A. (2022). “Deep Neural Networks Predict the Need for CT in Pediatric Mild Traumatic Brain Injury: A Corroboration of the PECARN Rule.” Journal of the American College of Radiology: JACR, S1546-1440.