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Advanced Neurology Artificial intelligence in epilepsy education

assessing the multifaceted aspects of epilepsy, potentially to analyze clinical and textual data within epilepsy care.
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missing nuances critical to patient care. Striking a balance NLP tools are advancing to better interpret unstructured
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between AI assistance and human expertise is necessary to data from patient records, notes, and academic literature,
prevent the erosion of clinical judgment. In addition, skill facilitating more streamlined and accessible epilepsy
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degradation is a risk; reliance on AI for routine tasks can information. Deep learning models, including few-shot
weaken healthcare providers’ ability to perform essential learning, metric learning, and capsule neural networks,
clinical functions independently. Continuous training have shown promise in tackling data scarcity and
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is needed to ensure professionals retain their expertise enhancing diagnostic accuracy in epilepsy. These enable
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and remain prepared for situations where AI tools may be robust pattern recognition even with limited datasets,
unavailable or malfunctioning. 68 which is particularly beneficial for rare and complex
Ethical issues in AI deployment encompass fairness, seizure disorders.
privacy, safety, transparency, and explainability. 64-67 In the EEG signal processing remains a critical challenge
pharmaceutical industry, the use of AI in drug discovery in these applications, particularly in noise removal and
has raised risks and ethical concerns, particularly regarding feature extraction. Ongoing efforts aim to design AI
patient care outcomes and data privacy. Addressing these prediction tools with high sensitivity and specificity to
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ethical challenges requires AI systems to be transparent, improve real-time seizure prediction, which could enable
accountable, fair, and genuine, especially in educational preemptive interventions and enhance patient quality of
settings where AI’s role is growing. The inclusion of life. For example, the development of mobile applications
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AI in medical training programs should focus on its for epileptic seizure detection based on EEG signals
complementary role to traditional learning methods, demonstrates the importance of user-friendly tools that
reinforcing critical thinking and clinical judgment skills leverage advanced algorithms for practical uses. 62
among neuro physicians and other healthcare professionals. One area of ongoing exploration is the integration
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The impact of AI on patient experiences and the patient- of multimodal data, such as EEG, ECG, and imaging
provider relationship is another crucial consideration. As data, to further refine seizure prediction algorithms.
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AI assumes a larger role in decision-making, patients may The potential for wearable technology and mobile health
perceive that decisions are increasingly driven by algorithms applications is being leveraged to support continuous
rather than human judgment, potentially weakening the monitoring and allow real-time data collection. Such
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personal connection and empathy central to healthcare. devices can potentially improve patient autonomy by
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A human-centered approach that integrates AI insights providing early warnings and individualized feedback
with personalized care is essential for maintaining trust and based on AI-driven analysis. Simultaneously, techniques
compassion in patient interactions. Furthermore, epilepsy for baseline removal in EEG signal processing are paving
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management requires personalized care plans that combine the way for more accurate and subject-independent
AI-generated insights with clinical expertise to meet the emotion classification. 69
complex needs of each patient. 68
Collaborative frameworks are also evolving to foster
To ensure the ethical use of AI in epilepsy care, data sharing and enhance epilepsy research across
continuous monitoring and adherence to ethical standards institutions. For example, federated learning enables
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are imperative. 64-68 Establishing regulatory frameworks, institutions to collaborate on AI models without sharing
conducting regular audits, and implementing ethical sensitive patient data directly, thereby protecting privacy
safeguards can help prevent unintended harm while ensuring while advancing collective knowledge. These frameworks
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that AI systems align with healthcare’s ethical principles. 64-68 are essential for scaling AI research and ensuring that AI
By recognizing and proactively addressing these ethical and models are trained on diverse datasets that reflect a broader
societal considerations, stakeholders can maximize AI’s patient population. Ultimately, as AI in epilepsy diagnosis
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potential to improve epilepsy care while safeguarding patient and management progresses, collaboration with healthcare
welfare. 64-68 This requires ongoing dialogue among healthcare providers and regulatory bodies is vital to ensure that
providers, patients, AI developers, and policymakers to navigate novel models or devices meet clinical standards, maintain
challenges and leverage the benefits of AI effectively. 64-68 patient safety, and support personalized patient care.
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13. Future research directions and With these advancements, AI can continue to evolve as a
transformative tool in epilepsy treatment and diagnosis,
emerging trends helping to bridge gaps in healthcare access, refine precision
Future research directions and trends in AI for epilepsy medicine approaches, and improve outcomes for epilepsy
education include using natural language processing (NLP) patients worldwide. 71

Volume 4 Issue 3 (2025) 24 doi: 10.36922/an.4777