Can Artificial Intelligence Enhance Syncope Management? A Multidisciplinary Collaborative Statement
Multidisciplinary consensus describes how AI and machine learning could enhance syncope diagnosis, risk prediction, and care delivery while addressing ethical and practical hurdles.
Key Findings
- Diagnostic augmentation. AI aids differentiation of true syncope from mimics, improving initial triage accuracy.
- Risk stratification. Predictive models can identify patients requiring admission versus safe discharge, supporting personalised care pathways.
- Decision support integration. Embedding explainable AI into clinician workflows reduces cognitive load and standardises evidence-based care.
Introduction
Syncope is common yet difficult to stratify—serious outcomes are rare, but the fear of missing them drives costly admissions. The statement evaluates whether AI can add precision without sacrificing safety.
Methods
Cardiology, emergency medicine, neurology, geriatrics, data science, and ethics experts synthesised evidence across AI modalities (ML, DL, NLP, wearable analytics) and mapped them to syncope workflows spanning triage, diagnostics, monitoring, and long-term management.
Results
Existing AI prototypes can separate true syncope from mimics, forecast short-term adverse events, and recommend targeted diagnostics, but most remain retrospective and lack prospective validation.
Discussion
Data fragmentation, bias, medicolegal liability, and explainability gaps limit deployment. Success depends on curated multicentre datasets, human-in-the-loop oversight, and clear governance of decision support tools.
Clinical Implications
AI augments clinician judgement by highlighting high-risk syncope patients for monitoring, streamlining low-risk discharges, and supporting longitudinal management plans.
Conclusion
AI will be most effective when it complements—not replaces—clinicians, with transparent algorithms embedded into evidence-based syncope pathways.
Future Directions
Establish federated learning ecosystems, compare AI-driven triage against existing risk scores in trials, and codify ethical frameworks for AI-supported syncope care.