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Advanced Neurology Artificial intelligence in epilepsy education

it essential for AI to fine-tune and customize learning predicting possible seizure occurrences based on historical
pathways; without this personalization, achieving mastery data and current EEG readings, thereby allowing patients
and timely interventions may prove difficult. AI tools, like and caregivers time to plan accordingly. Figure 2 shows
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ChatGPT, can aid in enabling simulation-based training the flowchart diagram illustrating the steps in EEG-based
by providing realistic and interactive scenarios that mimic epilepsy management using machine learning.
practical epilepsy cases, thereby improving diagnostic
accuracy and decision-making. The incorporation of AI 7. Comparative analysis of machine
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in SBL can also address the challenge of low-frequency, learning models for epilepsy detection
high-risk events, such as pediatric seizures, by allowing using EEG data
healthcare workers to practice and refine their skills AI is increasingly transforming epilepsy management
repeatedly in a controlled environment. This approach through applications that encompass diagnostic support,
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not only enhances technical and cognitive skills but also seizure prediction, treatment personalization, and patient
boosts self-efficacy and confidence among providers, education. Each AI model offers distinct advantages
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leading to improved performance in simulated settings. 40 tailored to different aspects of epilepsy care. For instance,
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It has been reported that AI-based automated seizure convolutional neural networks (CNNs) have become a
detection systems exhibit remarkable capabilities critical tool in processing EEG data, enhancing the precision
in developing models for accurate diagnosis and of seizure classification. 46,47 Similarly, recurrent neural
interpretation of EEG patterns, significantly reducing networks (RNNs) are well-suited for predicting seizures
the risk of human error. These systems can analyze due to their capability to interpret time-sequential data,
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vast amounts of EEG data within very short periods, which is critical in assessing patterns in EEG recordings. 46,47
consistently identifying subtle patterns that may elude Below is a comparative analysis of various AI models
human detection, thereby enhancing the overall efficacy of frequently applied in epilepsy management, highlighting
the diagnostic process. With AI support, the likelihood
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of misdiagnosis is significantly reduced, as these systems
are trained to identify specific patterns associated with
epileptic seizures, resulting in more consistent outcomes.
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The integration of AI within the SBL framework aligns
with the broader trend of using intelligent tutoring systems
and role-plays involving active agents that engage learners’
sense of responsibility. Continuous improvement of
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AI-driven SBL methods, incorporating feedback from both
learners and educators, could greatly benefit healthcare
professionals’ training in epilepsy management, ultimately
enhancing patient outcomes and healthcare delivery. 38,43
These advancements are expected to significantly improve
epilepsy education for medical professionals by providing
personalized, effective, and accessible learning experiences
through these training modules. 41
The ILAE Academy served as a structured online
learning venue featuring self-paced, interactive modules
based on competency-led curricula in epileptology,
catering to both entry and proficiency levels. 44,45 On this
case-based platform, AI can enhance the educational
experience through intelligent tutoring systems and
individualized feedback, alongside relevant clinical practice
and an online EEG and MRI reader complemented with
live-tutored courses for CME credits. AI’s capabilities in
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pattern recognition and data-driven decision-making can Figure 2. Electroencephalography-based epilepsy detection workflow
significantly improve the precision of disease diagnosis using machine learning Convolutional neural network; EEG:
Abbreviations:
CNN:
and treatment – which is vital in epilepsy management. Electroencephalography; RNN: Recurrent neural networks; SVM:
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Furthermore, AI is being used for seizure forecasting, Support vector machines.
Volume 4 Issue 3 (2025) 20 doi: 10.36922/an.4777