A research team at Florida Atlantic University (FAU) is applying Raman spectroscopy combined with artificial intelligence to detect skin cancer, according to a study highlighted by Newswise.

The approach targets a persistent clinical problem: distinguishing malignant lesions from benign mimics without an excisional biopsy. Raman spectroscopy works by measuring the inelastic scattering of laser light off molecular bonds in tissue. Because proteins, lipids, nucleic acids, and melanin each scatter light at characteristic shifts, the technique produces a biochemical fingerprint of a lesion — a so-called "optical biopsy" that requires no tissue removal and returns data in real time at the point of examination.

That fingerprint is where the artificial intelligence enters. Raw Raman spectra are complex, high-dimensional signals, and the differences between benign and malignant lesions can be subtle. Machine-learning classifiers can be trained to recognize spectral patterns associated with malignancy, effectively converting spectroscopic data into a diagnostic decision that a clinician could act on during a consultation rather than days later.

For dermatologists, the potential clinical implications are clear. Dermoscopy, the current first-line noninvasive assessment tool, retains meaningful rates of both false positives — driving unnecessary biopsies — and false negatives, particularly for amelanotic or otherwise visually ambiguous lesions. A spectroscopic adjunct validated against histopathology could reduce the biopsy burden for benign lesions while improving sensitivity for lesions that dermoscopy struggles to characterize. The technique is also nonionizing, unlike some imaging adjuncts, and does not depend on operator pattern recognition to the same degree as dermoscopy.

FAU has an established record in optical diagnostics research, and the Newswise listing positions this study as the latest step in that program. The full publication, released through Newswise, describes how the investigators coupled the Raman platform with AI-based analysis of the resulting spectra to classify skin lesions.

Caveats remain the standard ones for emerging diagnostic platforms. Spectroscopic classification must be validated against gold-standard histopathology across diverse lesion types, skin tones, and anatomic sites before it can support management decisions, and any algorithm trained on a limited spectral dataset risks performance degradation in broader clinical use. Regulatory clearance for an AI-assisted diagnostic device would require prospective, multicenter validation.

Readers can access the full study details through the Newswise release. If the platform holds up in validation, Raman-based AI diagnostics could shift initial skin cancer triage from visual heuristics toward objective, biochemically grounded measurement in the dermatology clinic.