AI-based EEG analysis: new technology and the path to clinical adoption

Area of Science:

• Medical Technology
• Artificial Intelligence in Medicine
• Clinical Neurology

Background:

• Artificial Intelligence (AI) and Machine Learning (ML) are increasingly explored for analyzing human electroencephalogram (EEG) data.
• Clinical integration of AI tools promises enhanced insights and efficiency but requires careful validation.

Purpose of the Study:

• To introduce AI-based clinical tools to a non-technical audience.
• To outline criteria for the successful integration of AI tools in clinical practice.
• To discuss challenges and opportunities of AI in clinical settings, using EEG as a key example.

Main Methods:

• Review of current literature on AI/ML in clinical EEG analysis.
• Drawing on 35 years of experience in medical device regulatory requirements.
• Analysis generalized to AI software with specific examples in epilepsy and neurological disorder monitoring.

Main Results:

• AI/ML in EEG analysis holds potential for improved clinical outcomes and efficiency.
• Successful integration necessitates addressing digital inequality, automation bias, and performance issues.
• Transparent development and collaboration between innovators and practitioners are vital.

Conclusions:

• AI-driven digital technology can revolutionize clinical practice, particularly in epilepsy monitoring.
• Careful consideration of development, validation, and interpretation is essential to prevent harmful misinterpretations.
• Collaboration is key for safe and effective adoption of AI in healthcare.