The integration of label-free vibrational spectroscopy into clinical workflows is entering a new phase, driven by advances in artificial intelligence, scalable instrumentation, and multimodal photonic technologies. Raman spectroscopy, providing highly specific molecular fingerprints, has evolved from a descriptive analytical method into a powerful platform for data-driven clinical decision support. Recent developments in laser technology, detection schemes, and fiber-based probe designs have enabled robust and compact Raman systems suitable for real-world medical environments. This includes advanced Raman modalities such as coherent Raman scattering, which enable rapid chemical imaging with high spatial resolution. In parallel, the integration of Raman techniques with complementary optical modalities such as two-photon excited fluorescence, fluorescence lifetime imaging microscopy or second harmonic generation enables multimodal imaging approaches that provide comprehensive biochemical and structural information in complex tissues. A key enabler of this transformation is artificial intelligence. Data-driven methods - including machine learning and deep learning - enable the translation of high-dimensional spectral data into clinically actionable outputs that remain robust under the variability of clinical settings. Rather than relying on isolated biomarkers, these approaches leverage the full molecular fingerprint, enabling scalable and reproducible diagnostics. These advances are demonstrated in two major application areas. In infection medicine, AI-assisted Raman spectroscopy of blood and related samples enables rapid molecular phenotyping of host–pathogen interactions, antibiotic resistance profiling supporting early and targeted therapeutic decisions. In oncology, fiber-based Raman probes and multimodal nonlinear imaging approaches enable real-time, label-free tissue characterization and open new opportunities for intraoperative guidance and molecularly informed surgery. Building on these clinically validated use cases, we introduce the concept of optical digital twins. By integrating longitudinal Raman molecular fingerprints with clinical and physiological data streams, dynamic patient-specific models can be established. These models enable the detection of subtle deviations from individual baselines and support predictive and preventive healthcare strategies. Together, AI-driven Raman spectroscopy establishes a foundation for personalized, data-centric medicine, where spectroscopy evolves from a diagnostic modality into a continuous monitoring and decision-support technology. | 
