Artificial intelligence (AI) is increasingly applied in healthcare, but concerns remain about bias affecting under‐represented groups. We investigated whether skin tone is systematically encoded in hyperspectral imaging data and how this affects classifications. Images were collected from 45 healthy women of the upper leg skin and vulvar mucosal tissue. Skin tones were grouped using the individual typology angle scale. Physiological parameters (oxygen saturation, haemoglobin, water and near‐infrared indices) were compared across groups. Unsupervised and supervised classification models were evaluated. Skin tone values ranged from −0.7 to 75.8 (20 very light, 9 light, 9 intermediate, 7 tan and 2 brown). All physiological parameters differed significantly across groups ( p < 0.001). Unsupervised learning achieved 38.5% balanced accuracy, whereas supervised learning reached 71.4%, with high accuracies for tan (94.6%) and brown (95.0%) groups. Skin tone influences HSI data; it may act as a confounder in AI models, underscoring the need for diverse datasets to ensure equitable performance.

Ovarian cancer (OC) is typically treated with cytoreductive surgery (CRS). Hyperspectral imaging (HSI) is an emerging non-invasive, label-free technique that enables whole-area scanning, making it a promising tool for real-time tumour recognition. However, developing tumour recognition algorithms requires a foundational understanding of spectral variability in normal tissues. This study focusses on the in vivo spectral profiles of key abdominal and pelvic organs encountered during CRS, including the uterus, ovaries, intestines, mesentery, omentum, peritoneum, and fallopian tubes, and evaluates the potential for organ recognition using HSI data. Intraoperative HSI data were from healthy patients. Two machine learning models, a support vector machine (SVM) and a 3D convolutional neural network (3DCNN), were trained to classify the organs based on their spectral signatures. In total, 15 patients were included in the dataset. The 3DCNN slightly outperformed the SVM in terms of the average accuracy (0.889 vs. 0.878), sensitivity (0.648 vs. 0.604), specificity (0.936 vs. 0.930), and Dice Similarity Coefficient (0.595 vs. 0.569). This study demonstrates the feasibility of using HSI for organ differentiation in the clinical setting, although in some cases separability remains a challenge, especially when organs have similar spectra. This is a critical step towards a generalizable in vivo abdominal tumour recognition algorithm, by carefully investigating spectral fingerprints of abdominal tissues.