Technical and regulatory challenges in artificial intelligence-based pulse oximetry: a proposed development pipeline

Pulse oximetry, although generally effective under ideal conditions, faces challenges in accurately estimating peripheral oxygen saturation (SpO2) in complex clinical scenarios, particularly at lower saturation levels and in patients with darker skin pigmentation. Artificial intelligence (AI) offers the potential to improve SpO2 monitoring by enabling more accurate, equitable, and accessible estimations. We highlight key challenges in developing AI-enhanced pulse oximetry, including the need for diverse and representative datasets, refined validation protocols addressing ethical concerns such as algorithmic bias, expanded SpO2 measurement ranges encompassing hypoxaemic levels, and enhanced model interpretability. We emphasise the importance of transitioning from subjective skin tone assessments to quantitative methods to ensure equity and mitigate bias. Finally, we propose a development pipeline and discuss strategies for robust, fair AI-based SpO2 monitoring, including aligning validation with global regulatory frameworks and fostering interdisciplinary collaboration. These advances will improve the reliability and fairness of pulse oximetry, ultimately contributing to enhanced global patient care.