Artificial intelligence (AI)-assisted photoplethysmography (PPG) technology has received considerable attention in recent years due to the rapid expansion of remote healthcare services, particularly during the COVID-19 pandemic. AI-enhanced PPG systems play an important role in improving vital sign assessment, enabling early disease screening, and supporting personalized healthcare. This bibliometric study (2014–2024) investigates global research trends, collaboration patterns, and regional disparities in studies related to AI-driven PPG technologies. The analysis reveals that China (20.4%), the United States (10.2%), and India (9.1%) are the leading contributors, with 1,160, 580, and 515 publications, respectively, while African countries contribute only a small fraction of the total research output. The results highlight emerging research activity in North Africa within this technological domain. However, systemic barriers such as limited funding and weak research infrastructure continue to hinder broader participation in advanced technological innovation. Addressing these disparities is essential for promoting equitable development of AI-based health monitoring technologies. Strengthening global collaboration and improving data accessibility will be critical for maximizing the potential of AI-enhanced PPG technologies in future healthcare systems.
- Charlton P.H., Kyriacou P. A., Mant J., Marozas V., Chowienczyk P., Alastruey J. Wearable Photoplethysmography for Cardiovascular Monitoring. Proceedings of the IEEE. 110 (3), 355–381 (2022).
- Kim S., Xiao X., Chen J. Advances in Photoplethysmography for Personalized Cardiovascular Monitoring. Biosensors. 12 (10), 863 (2022).
- Weng W.-H., Baur S., Daswani M., et al. Predicting cardiovascular disease risk using photoplethysmography and deep learning. PLOS Global Public Health. 4 (6), e0003204 (2024).
- Page M. J., Moher D., Bossuyt P. M., et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 372, n160 (2021).
- Moher D., Liberati A., Tetzlaff J., Altman D. G. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 6 (7), e1000097 (2009).
- Van Eck N. J., Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 84 (2), 523–538 (2010).
- Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement. 28 (3), R1–R39 (2007).
- Park J., Seok H. S., Kim S. S., Shin H. Photoplethysmogram analysis and applications: An integrative review. Frontiers in Physiology. 12, 808451 (2022).
- Mejía-Mejía E., Allen J., Budidha K., El-Hajj C., Kyriacou P. A., Charlton P. H. Photoplethysmography signal processing and synthesis. In: Kyriacou P. A., Allen J. (Eds.), Photoplethysmography. Elsevier (2021).
- Liu S.-H., Li R.-X., Wang J.-J., Chen W., Su C.-H. Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume. Applied Sciences. 10 (13), 4612 (2020).
- Demiscan D., Lozan O. The use of artificial intelligence in coordinating COVID-19 prevention measures at the territorial level. Moldovan Journal of Health. 11 (4), 44–48 (2024).
- Channa A., Popescu N., Skibinska J., Burget R. The rise of wearable devices during the COVID-19 pandemic: A systematic review. Sensors. 21 (17), 5787 (2021).
- Umair M., Cheema M. A., Cheema O., Li H., Lu H. Impact of COVID-19 on IoT adoption in healthcare, smart homes, smart buildings, smart cities, transportation, and industrial IoT. Sensors. 21 (11), 3838 (2021).
- Baker S., Xiang W. Artificial intelligence for things for smarter healthcare: A survey of advancements, challenges, and opportunities. IEEE Communications Surveys & Tutorials. 25 (2), 1261–1293 (2023).