Hidden Signals in Serum Protein Electrophoresis: Artificial Intelligence from Laboratory Pattern Recognition to...

Serum protein electrophoresis (SPE) remains a central laboratory method for the detection and characterization of monoclonal gammopathies and other dysproteinemic patterns. Despite substantial analytical automation, interpretation still relies largely on graphical representation and expert visual assessment, making low-amplitude or borderline abnormalities particularly challenging. Artificial intelligence (AI), including machine-learning (ML) and deep-learning (DL) approaches, has increasingly been applied to SPE, with high performance reported for automated recognition of established abnormalities, particularly M-proteins, i.e., monoclonal immunoglobulins or immunoglobulin fragments produced by clonal plasma cells. However, most models have been developed against predefined diagnostic targets and evaluated using expert interpretation, immunofixation, or immunotyping as reference standards. Improved recognition of known abnormalities should therefore not be equated with discovery of additional clinically meaningful information. Modern electrophoretic platforms generate machine-readable numerical signals that may preserve features not explicitly considered during routine graphical interpretation. This creates the possibility of extending AI-assisted SPE from conventional pattern recognition toward detection of low-amplitude, distributed, or sub-visual signal characteristics. Yet current literature has focused predominantly on algorithmic performance, while the validation pathway required to determine whether additional AI-detected features are biologically meaningful and clinically actionable remains insufficiently defined. Evidence is strongest for automated M-protein recognition, including low-amplitude abnormalities, whereas non-canonical systemic patterns, longitudinal signal analysis, and AI-assisted decomposition of therapeutic monoclonal antibody interference remain exploratory. This critical review addresses this translational gap by evaluating AI applications according to the nature of detected information, strength of supporting evidence, and proximity to clinical implementation. We propose a translational framework addressing four complementary domains: analytical validity, biological validity, clinical validity, and clinical utility. The evidence required in each domain depends on the intended claim and clinical use.