Abstract

Pulse oximetry, although generally effective under ideal conditions, faces challenges in accurately estimating oxygen saturation (SpO2) in complex clinical scenarios, particularly at lower saturation levels and in patients skin pigmentation. Artificial intelligence (AI) offers the potential to improve SpO2 monitoring by enabling more equitable, and accessible estimations. We highlight key challenges in developing AI-enhanced pulse oximetry, the need for diverse and representative datasets, refined validation protocols addressing ethical concerns algorithmic bias, expanded SpO2 measurement ranges encompassing hypoxaemic levels, and enhanced model pretability. 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 fair AI-based SpO2 monitoring, including aligning validation with global regulatory frameworks and fostering ciplinary collaboration. These advances will improve the reliability and fairness of pulse oximetry, ultimately uting to enhanced global patient care.