This paper introduces a fast and automated virtual reality (VR) system that generates patient-specific 3D models from CT and MRI scans for spine surgical simulation. The system allows surgeons and trainees to practice complex procedures in an immersive environment, enhancing surgical education and preoperative planning.

This study introduces a dual-modal AI framework combining CT radiology and H&E microscopy with clinical data for improved lung cancer diagnosis and subtype classification. The system demonstrates high accuracy and interpretability, offering a more robust and transparent approach to overcome the limitations of single-modality diagnostic methods.

This paper introduces an artificial intelligence system (AIOC) designed to accurately detect fetal orofacial clefts from ultrasound images and simultaneously enhance the expertise of radiologists. Trained on a large, multi-center dataset, AIOC demonstrates expert-level diagnostic performance and significantly improves junior radiologists' sensitivity, offering a scalable solution for early diagnosis and medical education in resource-limited settings.

This paper introduces AIRT, an end-to-end deep-learning framework that generates deliverable single-arc VMAT prostate treatment plans in less than one second. The method achieves plan quality comparable to clinical systems, significantly enhancing the efficiency and consistency of radiation therapy planning.

This paper introduces a transformer-based AI system utilizing the RT-DETR model to accurately detect fungal elements in potassium hydroxide (KOH) microscopy images for dermatophytosis, effectively distinguishing them from common artefacts. The system demonstrated high sensitivity and accuracy, suggesting its potential as a reliable automated screening tool in dermatomycology.

This paper proposes a secure and distributed system for lung disease diagnosis, specifically COVID-19 and Pneumonia, using a hybrid federated learning-enabled ensemble model. It combines established CNN architectures with the SWIN Transformer to enhance diagnostic accuracy and ensure patient data privacy through federated learning.

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.