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 paper re-evaluates the classic make-or-buy decision for enterprise software, considering the impact of rapidly advancing agentic AI systems. It analyzes how AI reshapes key decision factors, develops a typology of enterprise applications, and concludes that AI transforms the in-house "make" option into a hybrid governance form, arguing that the "SaaSocalypse" narrative is largely overstated.

This article proposes a practical framework for engineering measurable trust in clinical AI systems, moving beyond subjective impressions of model performance. It emphasizes integrating evidence, human supervision, and staged autonomy within a multi-layered architecture to ensure safety and accountability in healthcare applications.

This paper proposes QAROO, an AI-driven online task offloading framework for wireless-powered mobile edge computing (MEC) networks. It integrates quantum neural networks, attention mechanisms, and recurrent neural networks to co-optimize computing and energy resources, offering an efficient and stable solution for dynamic IoT environments.

This paper introduces an autoresearch framework that uses AI coding agents to optimize ground-state preparation protocols for quantum and quantum-classical many-body systems. It demonstrates how this method can effectively tune hyperparameters for VQE, DMRG, and AFQMC protocols, leading to improved energy proxies within fixed computational budgets across various spin models and molecular Hamiltonians.