AI-guided light analysis could help assess skin cancer and reduce unnecessary biopsies
Skin cancer is the most common cancer in the United States and among the most common worldwide. Nearly 1.5 million new cases were diagnosed globally in 2024, including nearly 340,000 melanomas. Nonmelanoma skin cancers—primarily basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)—are even more common, with 5.4 million cases diagnosed annually in the U.S.
Skin cancer is a prevalent issue worldwide, with millions of cases diagnosed each year. Traditional biopsy methods are invasive and costly, especially when used to screen for benign lesions that may prove to be cancerous. Researchers at Florida Atlantic University are investigating a new approach to detect skin cancer noninvasively, combining Raman spectroscopy—a technique that analyzes light scattering in tissue—with machine learning algorithms.
By applying a handheld Raman spectroscopy probe to clinical samples, the scientists generated over 1,000 Raman spectra to train machine-learning models in distinguishing between basal cell carcinoma, squamous cell carcinoma, and normal skin. Results published in Advanced Chemical Microscopy for Life Science and Translational Medicine 2026 indicate that several machine-learning approaches achieved around 84% accuracy in identifying cancerous tissue, with support vector machines showing promising sensitivity and specificity.
The study's lead author, Andrew Terentis, notes that while the findings are preliminary, they suggest a potential future where rapid, noninvasive diagnostics could guide clinical decisions and reduce unnecessary biopsies. The researchers plan to expand their investigations with larger studies and more advanced machine-learning techniques to improve diagnostic accuracy and practicality for clinical use.
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