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Advanced Neurology Artificial intelligence in epilepsy education

healthcare interventions. Healthcare professionals must 12. Ethical and regulatory challenges
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develop digital skills, understand legal and ethical issues, in AI-driven epilepsy management and
and enhance their eHealth literacy to promote the safe education: Balancing innovation with
and efficient integration of AI. Furthermore, language
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barriers in online medical education can be mitigated patient-centered care
through AI-generated multilingual educational materials, The integration of AI in epilepsy management and
ensuring global harmonization of healthcare practices and education raises several ethical and regulatory concerns
adherence to digital health standards. 64 that are essential to address for equitable and effective
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patient care. A major issue is bias and fairness; AI models
11. Potential negative aspects of AI in can inadvertently propagate biases embedded within the
epilepsy management training data, potentially causing disparities in treatment
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outcomes across diverse demographics. For instance,
AI systems can inadvertently perpetuate biases in training
data, leading to diagnostic inaccuracies for certain an AI system trained on data representing a particular
demographic may underperform for underrepresented
demographics. Research by Theodore et al. highlights groups, thereby amplifying existing healthcare inequalities.
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that biases in seizure data may result in inequitable epilepsy This emphasizes the need for diverse data representation to
care for underrepresented groups. In addition, using vast prevent unequal treatment and foster fairness in AI-driven
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medical data in AI raises privacy concerns. Timan and healthcare applications. Privacy and data security are also
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Mann emphasize that strict data protection protocols critical considerations, especially given the sensitive nature
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are essential to prevent misuse and maintain patient trust of patient data managed by AI in healthcare. In epilepsy
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in AI-driven treatments. Many AI models operate as diagnosis, vast amounts of labeled datasets containing
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“black boxes,” making it difficult to trace decision-making personal information are required, posing risks to personal
pathways. Wang et al. stress the need for transparent data privacy. Breaches in data privacy could lead to identity
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AI systems with defined accountability to ensure safe theft, discrimination, and diminished trust in healthcare
management in epilepsy care. Excessive reliance on AI systems. Furthermore, data transmitted over networks,
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could erode clinicians’ judgment and diagnostic skills. such as EEG recordings, are vulnerable to cyber-attacks.
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Shoeibi et al. suggest that balancing AI assistance with However, innovative strategies, like encrypted EEG data
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clinical experience is crucial to retaining essential skills classification using advanced algorithms and CNNs, show
for managing complex epilepsy cases. Routine use of promise in enhancing data security and privacy. This
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AI may diminish critical thinking, affecting clinicians’ highlights the importance of robust measures to safeguard
ability to manage epilepsy effectively without AI support. patient privacy and ensure data security in AI-based
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Continuous training is recommended to mitigate skill epilepsy care. Informed consent is fundamental for
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erosion. Assigning responsibility for AI-driven errors is patient autonomy; individuals must understand how their
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complex. Robust frameworks are necessary to address data will be used and the potential benefits and risks of AI
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accountability, especially in cases where AI misdiagnoses interventions. However, obtaining informed consent can
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epilepsy-related events. The use of AI may also be challenging due to the complexity of AI technologies,
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depersonalize patient interactions, potentially impacting which may hinder patients’ ability to fully comprehend the
patient satisfaction. Evidence-based data supports the implications of AI-driven care. Transparent communication
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necessity of human oversight in AI-augmented care and clear consent processes are necessary to uphold patient
to preserve empathy and trust, as epilepsy’s intricate rights and build trust. Transparency and accountability in
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management often requires personalized approaches that AI systems are also crucial. Many AI models function as
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AI alone may not fully address. Recent guidelines suggest “black boxes,” making it difficult to interpret how specific
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AI should support, not replace, individualized care. conclusions are reached. This lack of transparency can
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Ongoing audits and ethical standards are essential to obscure accountability, especially when adverse outcomes
prevent unintended harm. 62,69 Timan and Mann advocate arise. Regulatory frameworks that mandate transparency
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for regular evaluations to ensure AI aligns with ethical and define responsibility among healthcare providers,
healthcare practices. 64,66 Comprehensive regulations are AI developers, and system manufacturers are essential to
vital to ensure the safety, reliability, and fairness of AI address these challenges effectively. Over-reliance on AI
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applications in healthcare. Recent studies underscore in clinical settings can lead to “de-responsibilization,” where
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the need for robust national and international guidelines healthcare professionals may experience a decline in critical
to effectively govern the use of AI technologies in clinical thinking and clinical judgment. If professionals become
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settings. 64-69 overly dependent on AI tools, they may lose vigilance in
Volume 4 Issue 3 (2025) 23 doi: 10.36922/an.4777