AI-augmented MALDI-TOF MS screening reveals a high burden of undiagnosed monoclonal gammopathy in adult patients.

Monoclonal gammopathy of undetermined significance is a common precursor of multiple myeloma, yet its prevalence and clinical distribution remain poorly defined due to the limited scalability of conventional electrophoretic workflows. Here, we developed an artificial intelligence-augmented matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) platform that integrates machine learning, rule-based detection of weak monoclonal signals and glycosylation assessment for automated M-protein screening. Following training and validation on 5,218 retrospective serum samples, the platform was deployed in a real-world cohort of 12,263 adult patients. Screening identified M-proteins in 7.5% of patients, reaching 10.1% among individuals aged ≥ 50 years, and revealed a substantial burden of previously undetected monoclonal protein abnormalities. Beyond hematologic disorders, M-protein positivity was associated with a broad spectrum of infectious, neoplastic, and metabolic conditions. These findings establish a scalable strategy for population-level M-protein screening and highlight adult patients as an underrecognized high-risk population that may benefit from systematic surveillance.