Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy.

Area of Science:

• Neurology
• Artificial Intelligence
• Medical Informatics

Background:

• Neurology demands precise, evidence-based clinical decisions for optimal patient outcomes.
• Accurate assessment of patient disability is crucial in neurology subspecialties like Stroke and Epilepsy.
• Current practices require efficient tools to manage evolving neurological data.

Purpose of the Study:

• To review the principles and methods of artificial intelligence (AI).
• To explore current and developing AI applications in clinical neurology.
• To inform neurologists about AI's potential to enhance diagnostic insights and workflow.

Main Methods:

• Review of AI principles and terminology.
• Analysis of existing AI applications in neurological practice.
• Discussion of AI's role in integrating diverse patient data sources.

Main Results:

• AI can predict neurological impairment after Acute Ischemic Stroke (AIS).
• AI models assess the likelihood of IntraCranial Hemorrhage (ICH) expansion.
• AI aids in predicting clinical outcomes for comatose patients.

Conclusions:

• AI serves as a valuable tool to support neurologists' daily workflow.
• AI provides unique diagnostic insights by analyzing multimodal patient data.
• AI-based methods can complement existing tools for prompt, precise neurological decision-making and improved patient outcomes.