Biomedical vision models are increasingly used in clinical settings, but a significant challenge is their inability to generalize effectively due to dataset shifts—discrepancies between training data and real-world scenarios. These shifts arise from differences in image acquisition, changes in disease manifestations, and population variance. As a result, models trained on limited or biased datasets often…

Large language and vision models (LLVMs) face a critical challenge in balancing performance improvements with computational efficiency. As models grow in size, reaching up to 80B parameters, they deliver impressive results but require massive hardware resources for training and inference. This issue becomes even more pressing for real-time applications, such as augmented reality (AR), where…