Artificial intelligence in stroke management: From prevention to treatment

Area of Science:

• Neurology
• Medical Informatics
• Artificial Intelligence in Medicine

Background:

• Stroke care encompasses prehospital triage, acute management, rehabilitation, and secondary prevention.
• Artificial intelligence (AI) presents significant potential to enhance patient outcomes across all stroke care phases.
• Current applications of AI in stroke care include triage optimization, risk stratification, and personalized rehabilitation.

Purpose of the Study:

• To review the evolving role of artificial intelligence (AI) in comprehensive stroke care.
• To highlight AI's potential benefits in prehospital, acute, and rehabilitative stroke management.
• To discuss the challenges and future directions for AI integration in stroke care.

Main Methods:

• Review of existing literature on AI applications in stroke care.
• Analysis of AI's impact on prehospital triage, acute management, and rehabilitation.
• Discussion of deep learning, machine learning, robotics, and virtual reality in stroke care.

Main Results:

• AI-driven prehospital triage systems have reduced door-to-neurointerventional times by ~40 minutes.
• Deep learning improves accuracy and speed in analyzing brain scans for stroke lesion detection.
• AI facilitates personalized rehabilitation through robotics, virtual reality, and machine learning for motor, speech, and cognitive recovery.

Conclusions:

• AI shows promise in optimizing stroke care pathways and improving patient outcomes.
• Rigorous validation through multicenter trials and standardized benchmarks is crucial for AI integration.
• Future AI development must prioritize user-centered design, clinical workflow integration, and collaboration between developers and clinicians.