The Hope and the Hype of Artificial Intelligence for Syncope Management
European Heart Journal – Digital Health authors dissect where AI can realistically support syncope pathways and where today’s evidence still amounts to hype.
Key Findings
- Opportunity across the care continuum. AI can flag mimics during triage, predict near-term adverse events, and surface recurrence risk to guide outpatient follow-up when built on high-quality multicentre data.
- Evidence gaps dominate current deployments. Most published models are retrospective, lack prospective validation, and rarely benchmark against simple clinical scores, limiting confidence in routine use.
- Governance is non-negotiable. Ethical oversight, liability planning, and transparent communication with patients and clinicians are necessary to prevent automation bias and inequitable outcomes.
Introduction
Syncope remains a diagnostic dilemma: serious outcomes are rare yet unpredictable, leading to blanket admissions and costly workups. The point-of-view article explores whether artificial intelligence can clarify triage, diagnosis, and longitudinal management without inflating risk.
Methods
An international working group spanning cardiology, emergency medicine, neurology, geriatrics, electrophysiology, and data science reviewed contemporary literature, guideline gaps, and emerging AI tools—from risk scores and wearable analytics to natural language processing—to map credible use cases across the syncope journey.
Results
Surveyed prototypes demonstrate promise for differentiating true syncope from mimics, stratifying risk, integrating telemetry, and tracking longitudinal outcomes, yet few have cleared the thresholds of reproducibility, fairness, or cost-effectiveness for broad deployment.
Discussion
Bias in source data, fragmented health records, limited label quality, and opaque model behaviour remain structural barriers. The authors argue for hybrid workflows where AI augments rather than replaces human decision-making and is continuously monitored once live.
Clinical Implications
Health systems can pilot AI decision support to streamline resource use and patient counselling, but they must pair tools with clinician oversight, patient education, and post-deployment auditing.
Conclusion
AI’s promise in syncope is real but conditional: success hinges on high-integrity data, prospective evaluation, and embedding explainability into every step of the care pathway.
Future Directions
Priorities include building federated registries, conducting pragmatic trials that compare AI-supported workflows with existing risk scores, and codifying global governance frameworks for AI in syncope.