Application of Machine Learning in the Field of Intraoperative Neurophysiological Monitoring: A Narrative Review

Intraoperative neurophysiological monitoring (IONM) is being applied to a wide range of surgical fields as diagnostic tool protect patients from neural injuries that may occur during surgery. However, several contributing factors complicate the interpretation IONM, and it labor- training-intensive. Meanwhile, machine learning (ML)-based medical research has been growing rapidly, many studies on clinical application ML algorithms have published in recent years. Despite this, IONM remains limited. Major challenges applying include presence non-surgical factors, ambiguity definition false-positive cases, their inter-rater variability. Nevertheless, we believe enables objective reliable while overcoming aforementioned problems experts encounter. Large-scale, standardized technical considerations are required overcome certain obstacles use future.