PHAROS - Adaptation; Fairness; Explainability in AI Medical Imaging

PHAROS-AFE-AIMI aims to present innovative approaches for predictive modeling using large medical image datasets, emphasizing deep learning models and transparent, human-centered integration of GenAI and LLMs in health services. It tackles key challenges at the intersection of computer vision and healthcare AI, including multidisease diagnosis, model explainability, fairness, domain adaptation, continual learning. With rising interest in trustworthy and interpretable AI, PHAROS-AFE-AIMI fosters discussion on responsible deployment in sensitive applications. PHAROS-AFE-AIMI is organised under PHAROS AI Factory, ensuring its topics having real-world relevance and strong foundation in cutting-edge research. Finally, the workshop includes two challenges (Multi-Source-Covid-19 Detection and Fair Disease Diagnosis).