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2,807 papers
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.
Various deep learning architectures including the domain-specific pre-trained transformers for the task of legal sentence classification are explored, finding that domain-specific transformer models lead the row.
This review presents a critical comparative analysis of MSI and HSI-based LU/LC classification and prediction frameworks by systematically examining remote-sensing platforms, preprocessing requirements, ML/DL architectures, prediction models, validation strategies, and XAI integration under heterogeneous experimental c...
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AI assistants are increasingly used to support behavioural modelling from natural-language requirements, yet they provide little assurance that the resulting models are logically coherent. We argue that this limitation stems not only from the capabilities of large language models but also from the absence of explicit r...
It is concluded that AI literacy is no longer optional: the radiologist's role is not threatened by AI but transformed by it—from film reader to AI-augmented clinical imaging physician capable of extending world-class diagnostic care to underserved populations across India and globally.
The proposed dual-stream framework enables accurate and interpretable multi-label classification of dental restorations from panoramic radiographs and demonstrates robust generalisation across independent datasets, which support its potential as an AI-assisted clinical decision-support tool for dental radiographic inte...
Conventional artificial intelligence in dentistry is limited by a 'diagnostic-executive disconnect', functioning primarily as static 'bystander intelligence'. This review aims to delineate the technical architecture of Embodied Artificial Intelligence (EAI) in dentistry and analyze its potential to bridge this gap thro...
It is suggested that the moderate achievement gains reported for GenAI may mask weaker retention and transfer and poorer metacognitive calibration, especially under unstructured use, and that structured tasks requiring learners to explain or critique AI output should reduce maladaptive offloading.
The obtained results validate the proposed AI-DSS framework with AFO as a promising solution to cover the distance between accuracy in a controlled lab environment and reliability in the field, providing a strong, explainable, and resource-efficient solution to the digital sustainable agriculture problem.
A four-layer hybrid intelligence framework (FLHIF) that unifies cloud model uncertainty quantification, social network analysis of data lineage graphs, DeepSeek-V4-driven multi-agent semantic reasoning, and adaptive weight learning via proximal policy optimization, and adaptive weight learning via proximal policy optim...
For non-Hermitian Hamiltonians, gain, loss, and non-reciprocity produce complex eigenvalues which, in turn, facilitate different kinds of topological phases. One example is the imaginary line-gap phase, where eigenvalues cannot lie on the real line. This notion was recently shown to explain the robust temporal localisa...
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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