3 accepted papers
Accurate estimation of uncertainty in deep learning is critical for deploying models in high-stakes domains such as medical diagnosis and autonomous decision-making, where overconfident predictions can lead to harmful outcomes. In practice, understanding the reason behind a model’s uncertainty and t…
Deep learning has demonstrated expert-level performance in melanoma classification, positioning it as a powerful tool in clinical dermatology. However, model opacity and the lack of interpretability remain critical barriers to clinical adoption, as clinicians often struggle to trust the decision-mak
Artificial Intelligence (AI) has rapidly transformed the medical field, necessitating significant changes in medical education to prepare healthcare professionals for future work requirements. However, the integration of AI into medical curricula has been slow and lacks standardization. In this pape…