Ensuring equitable access to medical communication is crucial for deaf and hard-of-hearing individuals, especially in clinical settings where effective patient-doctor interaction is essential. In this work, we present a novel radar-based imaging framework for Sign Language recognition (with a focus on the Italian Sign Language, LIS), specifically designed for medical communication. Our method leverages 60 GHz mm-wave radar to capture motion features while ensuring anonymity by avoiding the use of personally identifiable visual data. Our approach performs sign language classification through a two-stage pipeline: first, a residual autoencoder processes Range Doppler Maps (RDM) and moving-target indications (MTI), compressing them into compact latent representations; then, a Transformer-based classifier learns temporal dependencies to recognize signs across varying durations. By relying on radar-derived motion imaging, our method not only preserves privacy but also establishes radar as a viable tool for analyzing human motion in medical applications beyond sign language, including neurological disorders and other movement-related conditions. We carried out experiments on a new large-scale dataset containing 126 LIS signs—100 medical terms and 26 alphabet letters. Our method achieves 93.6% accuracy, 87.9% sensitivity, 99.3% specificity, and an 87.7% F1 score, surpassing existing approaches, including an RGB-based baseline. These results underscore the potential of radar imaging for real-time human motion monitoring, paving the way for scalable, privacy-compliant solutions in both sign language recognition and broader clinical applications. The code is available at https://github.com/IngRaffaeleMineo/SignRadarClassification_MICCAI2025 and the dataset will be released publicly.