Excitation–Emission Fluorescence Spectroscopy Combined with Machine Learning for Biomedical Diagnostics: A Systematic Review

Excitation–emission matrix (EEM) fluorescence spectroscopy, when combined with machine learning algorithms, has emerged as a highly promising tool for non-invasive biomedical diagnosis, demonstrating significant potential across various applications. This systematic review offers a comprehensive analysis of recent advancements in integrating EEM with chemometric techniques and machine learning models for the detection of infectious diseases, cancer, neurological, and metabolic disorders, as well as for monitoring bioactive compounds and hormonal contaminants. The review examines multivariate approaches alongside spectral preprocessing strategies, highlighting their ability to resolve overlapping signals and extract relevant information from complex biological matrices. The reviewed studies report promising high sensitivity, specificity, and accuracy values across diverse biological matrices and disease targets, supporting the scalability and versatility of this diagnostic platform. A critical evaluation of methodological approaches is also provided, identifying common pipeline-level challenges and drawing a constructive distinction between proof-of-concept studies, which establish the discriminative potential of EEM spectral data and studies aimed at clinical validation, a distinction that helps contextualize reported performance and guides future research design. Future perspectives focus on the development of open-access spectral databases, portable devices, standardized preprocessing protocols, and the integration of deep learning and explainable artificial intelligence, all of which represent concrete pathways toward the clinical translation of EEM-based diagnostics. This review confirms the value of EEM spectroscopy coupled with machine learning as a versatile, scalable, and high-impact platform for biomedical diagnostics, with significant potential for applications in public health and personalized medicine.