E4   |   10 min   |   Latest   |  Publication Source    Podcast based on: Chiu T-M, Li Y-C, Chi I-C, Tseng M-H. AI-Driven Enhancement of Skin Cancer Diagnosis: A Two-Stage Voting Ensemble Approach Using Dermoscopic Data. Cancers. 2025; 17(1):137. https://doi.org/10.3390/cancers17010137Type: Article  |  Publication date: Jan 3, 2025 Summary: This research paper details the development and validation of an AI model for skin cancer diagnosis using dermoscopic images. A two-stage classification approach, employing an ensemble of pre-trained convolutional neural networks and vision transformers, significantly improved diagnostic accuracy and drastically reduced false negatives in both a Western (ISIC) and Eastern (CSMUH) dataset. The model distinguishes between melanoma, non-melanoma skin cancers, and benign cases, aiding clinicians in prioritising treatment. The study highlights the potential for AI to enhance skin cancer diagnosis, particularly in resource-constrained settings, though limitations regarding computational demands and dataset size are acknowledged. Keywords: malignant melanoma; dermoscopic images; voting ensemble learning; two-stage classification strategy Disclaimer: The content of this podcast is a summary and discussion of the original publication and does not represent the views of the authors or journal. The information shared here is intended for educational purposes only and does not constitute clinical advice or recommendations. It also uses AI-assisted summaries of the original work and may or may not contain innacuracies so we encourage listeners to consult the original publication for complete details.