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...
Shunichi Nakahara, Shunsuke Kondo, Abulhassan Ali et al.· Journal of Mechanical Ventil...· 0 citations
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.
Krithi Shailya, Ananya Lakshmi Ravi, V. VenkatanathanK. et al.· arXiv.org· 0 citations
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.
Tamara Gajić, Dragan Vukolić, M. Petrović et al.· Frontiers in Artificial Inte...· 0 citations
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.
Xin-Xin Li, Ji-Liang Yan, Na Li et al.· Frontiers in Endocrinology· 0 citations
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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.
Yi-Hui He, Dan-Yu Li, Dan-Bing Li et al.· Frontiers in Endocrinology· 0 citations
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.
Reshma Sheik, Adithya Kuppachi, S. Nirmala· Sādhanā· 0 citations
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...
C. S. Devi, M. Prabukumar· Applied Geomatics· 0 citations
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...
Radosław Klimek· Proceedings of the ACM/IEEE...· 0 citations
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.
Emily Das, Rasel Mondal, Ashim Dhor et al.· Indian Journal of Radiology...· 0 citations
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...
Z. Khurshid, A. Alshamrani, A. Alnaim et al.· International Dental Journal· 0 citations
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...
S. Wang, Huan-Cai Lin, Liangyue Pang· International Dental Journal· 0 citations
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.
Gaurav Shaileshkumar Dave· Quarterly review of distance...· 0 citations
The MIT professors collaborated to identify the first AI-discovered antibiotic, a scientific breakthrough that is widely recognized as a milestone in AI-driven drug discovery.
MIT News · Artificial Intelligence· news.mit.eduOct 30, 2026
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.
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.
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