Physics-Guided Artificial Intelligence for Raman Spectroscopic Brain Cancer Diagnosis: Multi-Center Clinical Validation and Instrumentation Optimization

Brain cancer remains one of the most challenging malignancies because of its extensive biological heterogeneity, diffuse infiltration into surrounding brain tissue, and the need for rapid intraoperative diagnosis while preserving neurological function. Conventional imaging modalities, including magnetic resonance imaging (MRI), computed tomography (CT), and histopathology, remain indispensable but are limited in providing real-time molecular information during surgery. Raman spectroscopy has emerged as a promising optical diagnostic technology because it provides label-free biochemical characterization based on the inelastic scattering of photons from endogenous molecular vibrations. However, the inherently weak Raman signal, tissue autofluorescence, instrument variability, and the high dimensionality of spectral data continue to restrict widespread clinical implementation. In this study, we present a quantitative comparative analysis of published multicenter clinical investigations together with a physics-guided framework for optimizing Raman spectroscopy systems used in brain cancer diagnosis. Unlike conventional narrative reviews, this work integrates optical physics, instrumentation engineering, electronic signal acquisition, and artificial intelligence into a unified analytical model. Clinical studies from university hospitals and research centers are comparatively evaluated according to patient cohort characteristics, excitation wavelength, spectrometer configuration, detector technology, machine-learning algorithms, and diagnostic performance. In parallel, the physical factors governing Raman signal generation—including laser wavelength selection, photon collection efficiency, detector quantum efficiency, signal-to-noise ratio, optical throughput, and fiber-optic probe design—are analyzed to establish their influence on diagnostic accuracy. The comparative analysis demonstrates that near-infrared excitation at 785 nm combined with thermoelectrically cooled CCD detectors and optimized fiber-optic probes provides the most favorable balance between Raman signal intensity, fluorescence suppression, tissue penetration, and clinical practicality. Artificial intelligence algorithms, particularly convolutional neural networks and ensemble-learning methods, consistently improve tissue classification by extracting subtle biochemical features that are difficult to identify using conventional statistical techniques. Integrating optimized Raman instrumentation with explainable AI establishes a robust platform for rapid intraoperative molecular diagnosis, tumor-margin delineation, and personalized neurosurgical decision support. The proposed framework highlights the importance of combining photonic engineering, spectroscopy, electronics, and computational intelligence to accelerate the clinical translation of Raman spectroscopy into precision neuro-oncology and provides a foundation for future multicenter prospective validation studies.