AI-enhanced non-invasive monitoring of critical biochemical analytes in perioperative care: a review of signal processing and clinical applications

Current perioperative monitoring of critical biochemical analytes predominantly relies on intermittent arterial blood gas analysis, which carries inherent risks of invasiveness and discontinuous data acquisition. Emerging evidence suggests that variations in electrocardiogram and photoplethysmogram waveforms may hold significant predictive value for detecting critical biochemical analytes changes. Advances in artificial intelligence analysis technologies have further accelerated the development of non-invasive monitoring tools, including real-time non-invasive blood glucose monitoring for diabetic patients and blood potassium monitoring for renal dialysis patients. This review highlights the urgent clinical need for non-invasive, continuous monitoring of the critical biochemical analytes during the perioperative period. It provides a comprehensive summary of current monitoring technologies and signals related to critical biochemical analytes, with a focus on their potential application in non-invasive blood gas monitoring. Based on existing evidence, key analytes such as serum potassium, serum calcium, lactate, and blood glucose have demonstrated robust research foundations and are the primary focus of this review. However, further clinical validation is urgently required to confirm their reliability and applicability in clinical settings. Integrating artificial intelligence with traditional monitoring systems has the potential to significantly enhance the precision, timeliness, and effectiveness of perioperative care. These findings suggest that AI-enhanced non-invasive monitoring could reduce unnecessary blood sampling while providing earlier detection of critical biochemical analytes disorder. Successful clinical translation requires standardized validation protocols and hardware-software co-development to address current limitations in measurement consistency and clinical workflow integration.