Will data analytics revolution finally bring SERS to the clinic?

Author：Ahmed Y.F. Mahmoud a , Alexandra Teixeira a,b,c , Marta Aranda a,d , Maria S. Relvas a,e , Sergio Quintero a,f , Maria Sousa-Silva a,n , Alexandre Chícharo a , Mengkun Chen g , Mohadeseh Hashemi g , Jason B. King g , James W. Tunnell g , Carlo Morasso h , Francesca Piccotti h , Fabio Corsi h,i , Malou Henriksen-Lacey j,k , Dorleta Jimenez de Aberasturi j,k,l , David M´endez-Merino m, Alfonso Rodríguez-Paton´ m, Sara Abalde-Cela a,n,* , Lorena Di´eguez a,n

ABSTRACT：Since its discovery, surface-enhanced Raman scattering has shown to be an ultrasensitive analytical tool. The technique was revolutionized with advances in micro/nanofabrication techniques. This resulted in the expansion of SERS applications in biosensing. However, SERS is still facing many challenges to be translated into the clinic. In this review, we will discuss how data analytics algorithms may be used to democratize the applications of SERS in translational medicine. This combination has the potential to create a new dimension of SERS in translational medicine to unlock the SERS access to the clinic, offering high precision and accuracy in evaluating or monitoring health and disease states. We believe that AI/ML may open new avenues towards the benchmarking of SERS (bio)sensors and their applications in the medical field.

Keywords:Surface-enhanced Raman scattering SERS Data analytics Machine learning Artificial intelligence Healthcare Clinic

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