AI-driven proteomics: unlocking early cardiac detection in POEMS syndrome

POEMS syndrome is a rare paraneoplastic multisystem disorder with significant morbidity when cardiac involvement occurs, yet diagnosis is often delayed until advanced stages. Recent advances in artificial intelligence (AI)-driven proteomics offer a transformative approach for early detection. Cardiac-specific biomarkers such as N-terminal pro-B-type natriuretic peptide, vascular endothelial growth factor, and growth differentiation factor-15 correlate with myocardial stress and fibrosis, providing diagnostic clues even when imaging is inconclusive. AI-enhanced mass spectrometry can identify previously undetectable protein isoforms and post-translational modifications linked to cardiac pathology, while machine learning algorithms facilitate rapid, high-accuracy interpretation of complex datasets. Despite limitations such as the rarity of cases, heterogeneous presentations, and integration challenges, standardized protocols, collaborative data sharing, and biomarker validation could establish AI-driven proteomics as a pivotal tool for improving early cardiac detection and prognosis in POEMS syndrome.