Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

Area of Science:

• Neurology
• Medical Informatics
• Artificial Intelligence

Background:

• Machine Learning (ML) and Artificial Intelligence (AI) are increasingly used to derive actionable insights from complex data.
• These technologies hold significant promise for enhancing clinical decision-making in various medical fields.
• The application of ML/AI in epilepsy is rapidly evolving, generating both excitement and a need for critical evaluation.

Purpose of the Study:

• To review recent advancements in ML/AI applications within the field of epilepsy.
• To provide practicing epileptologists with an understanding of the benefits and limitations of integrating ML/AI tools.
• To discuss practical, ethical, and equity considerations surrounding ML/AI in epilepsy care.

Main Methods:

• Review of recent developments in ML/AI for epilepsy diagnosis, prognosis, and treatment.
• Analysis of ML/AI applications including seizure prediction, detection, and treatment response monitoring.
• Discussion of ethical and practical challenges, including the use of Large Language Models (LLMs).

Main Results:

• ML/AI tools are being developed for diverse epilepsy-related clinical decisions, from risk prediction to treatment optimization.
• Current ML/AI tools show promise but often lack rigorous validation regarding transferability, effectiveness, and safety.
• Ethical considerations and equity issues are critical for the responsible development and deployment of these technologies.

Conclusions:

• ML/AI tools are poised to transform epilepsy practice, offering new avenues for patient care.
• Epileptologists must understand and critically evaluate ML/AI tools to leverage their benefits effectively.
• The future of epilepsy practice will likely involve a synergy between human expertise and advanced ML/AI capabilities.