Recent advancements in medical imaging are increasingly focused on enhancing interpretability and efficiency, addressing long-standing barriers to clinical adoption. Work on brain tumor analysis has introduced frameworks that combine explainable AI with high classification accuracy, allowing clinicians to understand model reasoning while diagnosing complex cases. In cervical spine imaging, a novel projection-based approach has streamlined fracture detection, achieving high accuracy with reduced computational demands. Unsupervised denoising methods for cardiac PET imaging have emerged, enabling clearer visualization of dynamic data without the need for paired training sets, thus preserving quantitative integrity. Additionally, techniques for reconstructing colonoscopic data are evolving to account for peristaltic motion, improving surgical navigation. Innovations in dental imaging have also tackled artefact reduction in cone-beam computed tomography, enhancing diagnostic quality. Collectively, these developments signify a shift towards more interpretable, efficient, and clinically applicable imaging solutions, with potential to significantly improve patient outcomes across various medical fields.