Category
2,336 papers
This Perspective defines radiology as an epistemic system: the organized clinical infrastructure through which imaging observations become warranted, actionable, revisable, and accountable knowledge.
This review provides a clinician-centered understanding of AI adoption, highlighting that acceptability depends not only on what AI can do but also on whether it can be integrated safely, ethically, and in ways that preserve professional judgment and therapeutic relationships.
BACKGROUND Artificial intelligence (AI) is increasingly being used in diagnostic veterinary anatomy to assist with medical-image interpretation, anatomical segmentation, disease recognition, and quantitative morphometric measurements. Even so, evidence specific to this area remains fragmented, particularly with regard...
Artificial intelligence for surface water quality prediction has evolved rapidly from classical regression and ensemble learning toward deep sequence, graph-based, physics-informed, and representation-learning approaches, while a largely disconnected systems literature has matured around digital twins, edge intelligenc...
Reach audiences
Advertise in front of researchers, engineers, and readers.
Hybrid printing operations integrate conventional manufacturing, additive manufacturing, and digital printing technologies, to create complex scheduling challenges arising from dynamic production environments, heterogeneous resources, and the limited transparency of conventional Artificial Intelligence (AI)-based sched...
We present a synthesis of the Karimov–Alekberli (KA) Framework into a unified theory ofConstitutional Intelligence: the capacity of an adaptive system to make consequentialdecisions that are simultaneously governed, explainable, accountable, reconstructable,verifiable, and capable of learning without retroactive falsif...
Hybrid printing operations integrate conventional manufacturing, additive manufacturing, and digital printing technologies, to create complex scheduling challenges arising from dynamic production environments, heterogeneous resources, and the limited transparency of conventional Artificial Intelligence (AI)-based sched...
Background Mechanical ventilation remains a complex intervention requiring continuous adjustment to balance oxygenation, ventilation, lung protection, patient–ventilator synchrony, and readiness for liberation. Artificial intelligence (AI) may support more individualized ventilator management by integrating multidimens...
KANEx is introduced, the first ever framework that leverages the symbolic transparency of KANs to ground VLM reasoning, and suggests that grounding linguistic explanations and visual attributions in mathematically interpretable units is a necessary step toward trustworthy medical AI.
The findings demonstrate that the organizational adoption of AI-enabled Digital Twins cannot be explained solely by conventional technology-acceptance factors but also depends on organizational digital capabilities and the institutional environment.
A long-time-series, multi-database knowledge map of AI applications in thyroid nodule diagnosis is established, systematically revealing the global research pattern, spatiotemporal evolution characteristics, and guideline-driven developmental logic of this field.
A comprehensive bibliometric analysis is conducted to characterize the AI-driven DR knowledge structure, collaboration networks, and hotspot migration, and to elucidate co-evolutionary dynamics among AI advances, data modality development, and DR research.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
GPT-Lab
· gpt-lab.eu
Oct 12, 2026
AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
