Advanced artificial intelligence combined with SERS platforms for diagnosis and therapeutic effects of cancer in clinical applications.

Surface-Enhanced Raman Spectroscopy (SERS) has become a valuable way to detect small amounts of molecules due to its high sensitivity. Nonetheless, applying it in the clinic has been slow because of issues with the spectrum's complexity, background noise, and differences in biological samples. Integrating Artificial Intelligence (AI) into SERS has made its use for diagnostics much better by allowing automation of spectral preprocessing, noise removal, key information extraction, and accurate classification. Traditional machine learning (ML) and advanced deep learning (DL) AI algorithms can effectively interpret complex SERS data and recognize the biomarkers specific to different diseases in non-invasive samples such as serum, saliva, urine, breath condensates, and exosomes. AI-SERS technology provides a quick, scalable way to find cancer at an early stage, classify the type of cancer, and track the effects of therapy, matching the goals of precision oncology. It clearly explains the fundamentals of SERS, AI-based techniques to signal analysis and how the two are employed together in oncology. We describe new progress toward diagnosing many cancers, assessing outcomes, and tracking how patients react to therapy. In particular, the review presents the use of AI-enhanced SERS platforms for cancers of different types, such as breast cancer (BC), lung cancer (LC), prostate cancer (PCa), skin cancer, oral cancer, gastrointestinal cancers, colorectal cancer (CRC), pancreatic cancer (PaC), and ovarian cancer (OvCa). We also discuss how these platforms are being used for early diagnosis, monitoring treatment effects, and predicting the possibility of the disease returning. Ultimately, we discuss how key translational challenges like data standardization, the ability to explain models, and the need for approval by authorities must be dealt with before AI-SERS can be used routinely in clinical oncology.