A 2022 study proposed a deconvolution algorithm to more accurately distinguish true conduction block from temporal dispersion in nerve conduction studies of CIDP and MMN patients — a distinction that matters because conduction block is a key diagnostic criterion for MMN, and current clinical methods can be imprecise. This is a computational/quantitative method (not a trained AI model in the deep-learning sense) that could improve diagnostic accuracy if validated in larger, prospective studies. Broader AI/machine-learning applications specifically to MMN — such as AI-assisted antibody design or AI-based nerve conduction classification — do not yet appear in the published literature for this disease specifically, though such methods are active elsewhere in neuromuscular diagnostics and antibody drug discovery generally.