This paper introduces ePAI, an AI-powered system designed for the early and prediagnostic detection of pancreatic ductal adenocarcinoma (PDAC) from routine computed tomography (CT) scans. The system demonstrates high accuracy in detecting small lesions and significantly outperforms radiologists, offering a median lead time of 347 days before clinical diagnosis.

This paper introduces EndoRare, a novel one-shot generative framework that synthesizes diverse, high-fidelity images of rare gastrointestinal lesions from a single reference. It significantly enhances the diagnostic accuracy of AI models and improves the training of novice clinicians by providing realistic and varied case examples.

This paper introduces a novel method for early skin cancer classification that leverages a soft voting ensemble of Convolutional Neural Networks. The approach combines data preprocessing, image segmentation, and an ensemble of MobileNetV2, VGG19, and InceptionV3 to achieve high accuracy and balanced performance for real-world dermatological applications.

This paper systematically investigates fairness in medical imaging AI benchmarking competitions, revealing significant biases in dataset composition and critical flaws in data accessibility, licensing, and documentation. The findings highlight a disconnect between leaderboard success and clinically meaningful AI, urging for improved transparency and reusability standards.

This study explores the application of deep learning techniques in detecting and segmenting brain tumors from MRI scans, achieving significant improvements in accuracy and efficiency.