Integrating artificial intelligence into Korean medicine: Insights from neural and pulse signal analysis

As conventional medicine diagnoses patients to treat their diseases, Korean Medicine (KM) has traditionally relied on pattern identification, a diagnostic framework that classifies patients into clinically meaningful subgroups based on symptom patterns, pulse qualities, and abdominal and tongue observations.Although pattern identification techniques have guided personalized treatments for centuries, unlike diagnoses based on pathological or histological examinations, their dependence on subjective practitioner interpretation limits reproducibility, standardization, and integration into modern healthcare systems.Despite recent efforts to develop clinical guidelines, these initiatives remain insufficient for establishing a standardized decision-making process for pattern identification-a critical prerequisite for developing consistent treatment regimens.The rise of data-driven approaches combined with artificial intelligence (AI) technologies in contemporary biomedicine underscores the urgent need for quantitative tools that can complement and enhance traditional KM knowledge.Brain imaging techniques (e.g., electroencephalography [EEG], functional Magnetic Resonance Imaging [fMRI], functional nearinfrared spectroscopy [fNIRS]) have the potential to improve disease diagnosis and provide objective measures of subjective symptoms, such as pain.In addition, signals from the heart (e.g., electrocardiogram [ECG], pulse waveforms, blood pressure), lungs (e.g., respiratory rate), and muscles and skin (e.g., electromyography, skin conductance response, skin temperature) have also contributed to patient care, either independently or in combination.Nevertheless, these signals have distinct characteristics, and interpreting the physiological states they represent requires specialized expertise, with research still limited regarding the relationships or causal links between these biosignals and pattern identification.Recent advances in AI and biomedical signal processing offer promising solutions.Machine learning algorithms can capture subtle physiological variations from multimodal biosignals, spanning from the brain to the body. 1,2In this context, applying AI to KM diagnostics could enhance objectivity, reproducibility, accuracy, and efficiency while bridging traditional diagnoses with biosignals.Specifically, this commentary examines how AI-based analyses of brain