Artificial intelligence in next-generation cardiac systems: wearable and implantable technologies for clinical practice

Cardiac technologies have evolved from conventional diagnostic tools to implantable, wearable, and increasingly AI-enabled systems that support more continuous cardiovascular assessment. However, this transition should not be interpreted as a uniform improvement in clinical care. This critical narrative review examines contemporary cardiac monitoring and therapeutic technologies, with particular emphasis on implantable devices, wearable platforms, and artificial intelligence (AI) applications in rhythm analysis, imaging, and risk prediction. Across these domains, evidence maturity, diagnostic performance, and implementation readiness remain highly variable. Implantable systems provide the most continuous monitoring and, in selected cases, therapeutic intervention, but their benefit depends on indication, patient selection, complication profile, and follow-up infrastructure. Wearable technologies broaden ambulatory surveillance, especially for rhythm assessment, yet their clinical value is limited by false-positive alerts, user dependence, signal constraints, and downstream workflow burden. AI has shown promise in ECG interpretation, echocardiographic quantification, and analysis of longitudinal device-derived data, but many applications remain retrospective, insufficiently validated, or highly dependent on local workflows. A recurring limitation across next-generation cardiac systems is that technical capability often exceeds demonstrated real-world utility. Future progress will depend not only on innovation, but also on stronger validation, careful integration into clinical pathways, transparent reporting, and closer attention to interoperability, reimbursement, and data governance.