Google Research introduced PhotoScan, an investigational deep-learning system that estimates body-fat percentage, Android-to-Gynoid fat ratio and Visceral-to-Subcutaneous fat ratio from standard 2D smartphone photos. The system was pre-trained on more than 35,000 UK Biobank participant records and fine-tuned using a cohort of 677 adults. It is intended to estimate body-composition markers associated with insulin resistance, which is commonly defined by a HOMA-IR score above 2.9 and can precede type 2 diabetes by years. In five-fold cross-validation on the PhotoBIA cohort, PhotoScan produced a mean absolute error of 2.15 for body-fat percentage, compared with 2.91 for smartwatch bioelectrical-impedance analysis. Independent validation in the MetabolicMosaic cohort produced errors of 2.13 for body-fat percentage, 0.085 for the Android-to-Gynoid ratio and 0.085 for the Visceral-to-Subcutaneous ratio. A gradient-boosting model using demographics plus PhotoScan metrics reached an AUROC of 0.760 for identifying insulin resistance, versus 0.692 for demographics alone and 0.773 for demographics plus DXA scan data. Adding smartwatch bioelectrical-impedance data did not improve insulin-resistance classification. Google describes PhotoScan as a research prototype rather than a clinical screening product.