Adaptive Prompt Evolution for Continual Learning in Diabetic Retinopathy Detection
Published in SN Computer Science (Springer Nature), 2026
Recommended citation: Bravo-Rocca, G., et al. (2026). "Adaptive Prompt Evolution for Continual Learning in Diabetic Retinopathy Detection." SN Computer Science. https://link.springer.com/article/10.1007/s42979-026-05036-y
Adaptive Prompt Evolution (APE) enables autonomous prompt evolution for continual learning in privacy-sensitive healthcare applications, achieving 99% storage reduction while maintaining 96.9% accuracy across diverse patient populations. This work extends the TADIL research line with a focus on resource-efficient agent adaptation.
Recommended citation: Bravo-Rocca, G., et al. (2026). “Adaptive Prompt Evolution for Continual Learning in Diabetic Retinopathy Detection.” SN Computer Science.