4 reasons neurologists can trust AI for EEG

Can neurologists trust AI for use in EEG?

The integration of artificial intelligence (AI) technology with human EEG interpretation has been met with a mix of excitement and apprehension. While the benefits of AI for boosting efficiency and accuracy are broadly accepted, neurologists, epileptologists, neurodiagnostic personnel, and other professionals may still lack confidence in how the technology will perform when it comes to actual patient care.


Regardless of specialty, most health professionals agree that the successful adoption of AI in any scenario¹ relies, first and foremost, on one important element: trust. This article offers four compelling reasons neurologists can trust AI for EEG, and the aspects of human/machine teaming that will continue to improve this already effective partnership



1. AI is a proven technology

Understanding any technology is essential to building trust in its performance. At its core, machine learning is a long-used AI subset that involves training algorithms to learn patterns from existing data sets. Machine learning models are exposed to various examples, and they adjust their parameters to recognize underlying patterns that help them make accurate predictions and/or classifications. In the case of EEG analysis, machine learning algorithms can be trained on massive datasets of EEG recordings, enabling them to recognize subtle patterns indicative of various neurological conditions.


Deep learning models, also called neural networks, capture intricate relationships within complex data. Deep learning algorithms are particularly adept in processing and analyzing EEG data’s intricate temporal and spatial relationships². This technique further enhances the AI tool’s ability to rapidly uncover nuanced patterns within EEG recordings and perform interpretation tasks more expediently.


Deep learning is the next logical step in strengthening the partnership between neurology and AI, moving beyond spike and seizure detection to using big data resources to support advanced AI applications for pattern recognition. Neurologists have noted the remarkable potential of deep learning for EEG analysis, with recent studies using the SCORE-AI model³ reinforcing the method’s accuracy and efficiency. Eventually, AI tools will combine automated EEG analysis with continuous brain monitoring,4 reducing the effort required for neurologists to diagnose and treat these time-critical conditions accurately.



2. Big data already exists

Big data refers to extremely large data sets that must be analyzed computationally. The three defining properties or “3 V’s” of big data are volume, velocity, and variety, which refer to the amount of data, the speed of data processing, and the different types of data within any given repository. Within neurology, massive data sets of annotated EEG recordings have already been gathered and validated by credible organizations around the globe, and new data is added continually.


Big data for EEG encompasses a wide array of neurological conditions, brain activity patterns, and responses to stimuli, providing ample learning ground for AI to identify intricate patterns and correlations that might take humans hours to analyze and interpret. By learning from vast amounts of annotated EEG recordings, AI algorithms can identify subtleties and abnormalities indicating conditions like epilepsy, sleep disorders, and brain injuries that may go unnoticed when using conventional methods for interpretation. This significantly boosts the performance and efficiency of neurology teams, reducing human time spent on repetitive, time-consuming tasks.



3. Robust adoption protocols are in use

Like any other medical device technology, AI tools are already vetted via extensive research and development and are subject to regulatory disciplines. AI algorithms are rigorously tested on diverse datasets5 to validate their accuracy and efficacy. From there, neurologists and other providers will adhere to protocols that provide appropriate periods of parallel use, validate tool outcomes and encourage skill building.


These protocols are designed to ensure patient safety, protect privacy, and promote AI tools’ seamless and successful integration into day-to-day operations. Healthcare professionals also continue to provide new protocols for AI adoption, identifying key considerations for adopting AI-based tools into clinical practices6. These considerations span across various themes, including cultural factors, data and algorithm validation, training, and education, and even the current level of AI acceptance within a provider or practice.


A phased implementation strategy must be used to build confidence and trust in the technology, with AI tools initially supporting neurologists in specific tasks before gradually expanding to encompass broader applications. This staged approach allows for iterative refinement based on real-world experience, reflecting the meticulous, patient-centered approach that defines healthcare innovation.



4. The Benefits of Human/Machine Teaming

More trust is built when AI is viewed as a supportive technology that amplifies human capabilities. Neurologists possess a wealth of clinical experience and expertise that AI lacks, and this expertise is invaluable for contextualizing AI-generated insights. Various studies and real-world applications exemplify the synergy between human neurologists and AI. This collaborative approach can significantly streamline the interpretation process, leading to more accurate analysis, faster diagnosis by the physician, and better patient outcomes.


Neurologists, epileptologists, and other neuro experts are only beginning to understand how big data and AI can be used to drive better health outcomes in the future. For example, researchers are currently working with AI and EEG data7 to help clinicians identify potential underlying epileptiform activity in children displaying abnormal behaviors. Within neurosurgery, the multifaceted integration of AI in neurology underscores its potential to reshape clinical practices and neurosurgical techniques.8


In the realm of seizure disorders, machine learning can predict epilepsy surgery outcomes with up to 90% accuracy, while automatic seizure detection using AI techniques enhances scalp EEG analysis. AI’s role extends to neuro-oncology, where it supports noninvasive glioma grading through MRI data analysis. And within the IDD space, AI-driven brain–machine interfaces can enable disabled individuals to interact with their environment using brain signals.


AI is also being used to predict the need for CT scans9 in mild pediatric TBI, where overuse of imaging and radiation may pose problems. Given the enormous amount of data available, the applications for EEG interpretation using big data and AI are endless.


With even more neuro AI tools just around the corner, the journey from early machine learning algorithms to today’s sophisticated AI-powered technology has already paved the way for a deep relationship of trust and cooperation between AI and neurology. As AI becomes more integrated with daily practice, neurologists, epileptologists, and neurodiagnostic teams can elevate their work to even higher levels of accuracy and efficiency. The use of AI for EEG interpretation is not only a technological advancement, but also an evolving partnership that will increasingly benefit the field of neurology.

practical applications of artificial intelligence in EEG



1. “A Better Way to Onboard AI.” Harvard Business Review, 28 Apr. 2022, hbr.org/2020/07/a-better-way-to-onboard-ai
2. Yannick Roy et al 2019 J. Neural Eng. 16 051001DOI 10.1088/1741-2552/ab260c
3. JAMA Neurol. 2023;80(8):805-812. doi:10.1001/jamaneurol.2023.1645
4. Front. Hum. Neurosci., 12 March 2019 Sec. Brain Imaging and Stimulation. Volume 13 – 2019
5. Yannick Roy et al 2019 J. Neural Eng. 16 051001DOI 10.1088/1741-2552/ab260c
6. “Trustworthy Adoption of AI in Healthcare.” DNV, www.dnv.com/research/healthcare-programme/data-sharing.html. Accessed 24 Aug. 2023.
7. Donoghue, Dr. Jacob. “Transforming Epilepsy Clinical Trials with EEG and Machine Learning.” Drug Discovery and Development, 17 Mar. 2023,
8. Ganapathy Krishnan, Abdul Shabbir Syed, Nursetyo Aldilas Achmad “Artificial intelligence in neurosciences: A clinician’s perspective” Neurology India 2018, Volume 66, Issue Number 4, Page 934-939
9. “Top 4 Ai Use Cases in Neurology in 2023.” AIMultiple, research.aimultiple.com/neurology-ai/. Accessed 24 Aug. 2023.