May 2026 in “International Journal of Drug Delivery Technology” This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.
June 2026 in “International Journal of Innovative Technologies in Social Science” In this narrative review, the authors highlighted that AI-supported electronic health record analysis could help recognize Polycystic Ovary Syndrome/Polyendocrine Metabolic Ovarian Syndrome earlier, but also warned that these systems might perpetuate historical biases if not properly validated and interpreted.
June 2026 in “Zenodo (CERN European Organization for Nuclear Research)” In this study, researchers developed a hybrid deep learning model called ScalpViT that accurately diagnosed scalp diseases with 94.3% accuracy, surpassing existing methods like ResNet-50 and EfficientNet-B3, and providing visual explainability for clinicians using GradCAM and Attention Rollout techniques.
June 2026 in “Zenodo (CERN European Organization for Nuclear Research)” This study introduces ScalpViT, a new deep learning model that accurately diagnoses visually similar scalp diseases with 94.3% accuracy, outperforming other methods like ResNet-50 and EfficientNet-B3, and providing dual visual explainability through GradCAM and Attention Rollout, potentially benefiting diagnosis in resource-limited settings in India.
This study reviewed the use of self-supervised Auto ML models for detecting alopecia areata, finding significant advancements in automated diagnosis but also challenges such as model explainability and data bias, which may guide future AI-driven dermatological diagnostics.