Review of applications of artificial intelligence in bioimpedance analysis

Bioimpedance analysis serves as a non-invasive modality for evaluating human body composition and fluid balance. Despite its clinical translational potential, traditional empirical regression models remain vulnerable to physiological variability, demographic heterogeneity, and measurement noise, which inflate estimation error and restrict generalizability. In this review, we present an evidence-based narrative synthesis of artificial intelligence (AI) methodologies integrated into bioimpedance frameworks to investigate these limitations. We analyzed how machine learning algorithms optimize non-linear Cole parameter extraction and equivalent circuit modeling under noisy spectral conditions without requiring rigid baseline assumptions. Furthermore, deep learning architectures such as convolutional networks and physics-informed models were examined for their capacity to resolve ill-posed inverse reconstruction problems in electrical impedance tomography and to process multidimensional sensor streams. Rather than presuming established clinical maturity, we critically scrutinized study-level translational readiness, highlighting pervasive methodological bottlenecks, including the reliance on small, single-center cohorts, risks of data leakage from sample-level cross-validation, hardware-induced motion artifacts, and an absence of independent external multicenter replication. By contextualizing emerging paradigms, including Explainable AI, uncertainty quantification, and decentralized learning within a realistic clinical taxonomy, we outline the methodological and validation standards necessary to advance AI-driven bioimpedance from exploratory proof-of-concept demonstrations toward reproducible, evidence-based clinical evaluation.