Work

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

Year: 2025

Type: article

Abstract: 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.

Source: UPM Digital Archive (Technical University of Madrid)

Authors: Cabanas Plana, Ana María, Martín Escudero, Pilar, Pagán Ortiz, Josué, Mery, Domingo

Institution: Pontificia Universidad Católica de Chile

Language: English

Topic: Non-Invasive Vital Sign Monitoring

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: green

Funder: Agencia Nacional de Investigación y Desarrollo

Awards: G1278513833, FORTALECIMIENTO DE LA INVESTIGACION INTERDISCIPLINARIA EN FISICA MEDICA Y DEL FUTURO DOCTORADO EN CIENCIAS MENCION FISICA Y DEL MAGISTER EN FISICA MEDICA EN DIAGNOSTICO POR IMAGEN DE LA UNIVERSIDAD DE TARAPACA, ENHANCING EQUITY IN COMPUTER VISION AND DEEP LEARNING: INTEGRATING SKIN COLOR MEASUREMENTS FOR FAIR AND DIVERSE DATASETS, G6182763333, NATIONAL CENTER FOR ARTIFICIAL INTELLIGENCE RESEARCH (NAIR)