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2,807 papers
This framework proposes a testable approach for future quantification of persistent internal mercury exposure, requiring validation through longitudinal biomonitoring, PBPK calibration, and mechanistic endpoint integration.
These convergent classification, attribution, and nodal-efficiency findings converge on the cerebellum-alongside the thalamus and hippocampus-as explainable-AI-identified regional substrates whose nodal efficiency tracks DSM-5-defined symptom severity in IGD.
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.
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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...
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...
Artificial intelligence-supported ventilator management is a rapidly evolving field with potential to enhance individualized respiratory care and future studies should prioritize external validation, prospective trials, workflow integration, explainability, and safety frameworks to determine whether AI can improve clin...
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.
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
GPT-Lab
· gpt-lab.eu
Oct 30, 2026
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
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