The Ghost in the Machine: Artificial Intelligence in Neurocardiology Will Advance Stroke Care

Abstract

Background: Innovations in artificial intelligence (AI) and machine learning (ML) are poised to transform stroke care, particularly for Neuro-Cardiac Programs (NCP) within both academic and community hospital systems. Purpose: Given AI’s success in large-vessel occlusion (LVO) detection and perfusion mapping delivered to our smartphones, the next leap for this “Ghost in the Machine” technology seems to be into the world of NCP: AI-enhanced logistics have started to help with cardiac monitoring after cryptogenic, large-artery and small-vessel stroke, looking for atrial fibrillation (AF) with an insertable loop recorder (ILR) and/or external patch. Results: The ‘CONNECT’ study from UCSD demonstrated that AI can increase protocol efficiency and reduce patient wait-times for ILR; with more AF detected, fewer strokes may result as more patients receive anticoagulation or Left Atrial Appendage Closure (LAAC). Conclusion: Therefore, organically, the next AI and ML-enhanced NCP frontier may involve inter-departmental “Shared Decision-Making” (SDM) process with LAAC, and/or Patent Foramen Ovale (PFO), in appropriately selected patients. In this editorial, we explore AI’s capability to disrupt current antiquated siloed communication tools, refine and streamline SDM processes and tailor patient-specific treatment plans, nevertheless advocating for intercalation of AI into NCP pathways in a secure, ethically-guided manner.