Frontiers in Artificial Intelligence | New and Recent Articles https://www.frontiersin.org/journals/artificial-intelligence RSS Feed for Frontiers in Artificial Intelligence | New and Recent Articles en-us Frontiers Feed Generator,version:1 2026-09-06T14:26:48.908+00:00 60 https://www.frontiersin.org/articles/10.3389/frai.2026.1852439 https://www.frontiersin.org/articles/10.3389/frai.2026.1852439 <![CDATA[Predicting first falls among older adults with chronic conditions and polypharmacy using routinely collected health records]]> 2026-09-04T00:00:00Z Original Research Igor LarrañagaMiguel RujasItxaso AlayoIrati ErreguerenaMarco CapoLaura Lopez-PerezMarta M. MediavillaMaría Dolores MartínezHéctor Chuliá-GilEleni I. GeorgaMuhammad Salman HaleemLeandro PecchiaDimitrios I. FotiadisGiuseppe FicoAne Fullaondo https://www.frontiersin.org/articles/10.3389/frai.2026.1840721 https://www.frontiersin.org/articles/10.3389/frai.2026.1840721 <![CDATA[A robust privacy-preserving federated framework for kidney CT image classification using transfer learning models]]> 2026-09-04T00:00:00Z Original Research Sai Sri Hemantha KonalaSrinivas Koppu https://www.frontiersin.org/articles/10.3389/frai.2026.1883853 https://www.frontiersin.org/articles/10.3389/frai.2026.1883853 <![CDATA[An agentic AI framework connecting language models to electronic health records and a biomedical knowledge graph for real-world evidence]]> 2026-09-04T00:00:00Z Original Research Gianmarco BellucciWanjun GuPeter W. RoseSergio E. Baranzini 7 million subjects), with the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a curated knowledge graph integrating relationships biomedical concepts from expert-maintained resources. Using this implementation (MedCP), we evaluated the approach across 100 benchmarking clinical research tasks, 617 biomedical factual accuracy questions (BiomixQA), an integrative case study linking clinical co-occurrence with molecular similarity, and a standardized protocol for generating and replicating real-world studies.ResultsAcross the 100-task benchmark, knowledge-graph access improved mean scores overall for GPT-5.5 and Claude Opus 4.8, though the pattern differed between models; on BiomixQA, SPOKE grounding raised multiple-choice accuracy for both models, without changing true/false accuracy. In the case study, disease co-occurrence patterns extracted from the EHR correlated with molecular network similarity, surfacing mechanistic hypotheses from real-world data. Applied to the replication of published observational studies, the research protocol compressed timelines from months to hours, lowering the technical barrier to query generation and execution while study design and interpretation remained under expert supervision.ConclusionsAn agentic AI infrastructure that combines institutional EHR data with curated biomedical knowledge via MCP can serve as a transparent, domain-grounded, supervised research assistant for real-world evidence, supporting both hypothesis generation and testing.]]> https://www.frontiersin.org/articles/10.3389/frai.2026.1965611 https://www.frontiersin.org/articles/10.3389/frai.2026.1965611 <![CDATA[Correction: Editorial: Digital medicine and artificial intelligence]]> 2026-09-03T00:00:00Z Correction Mini Han WangNorberto Peporine LopesNuno S. Osório https://www.frontiersin.org/articles/10.3389/frai.2026.1848787 https://www.frontiersin.org/articles/10.3389/frai.2026.1848787 <![CDATA[Deep convolutional neural network-based automated identification and classification of mungbean foliar diseases]]> 2026-09-03T00:00:00Z Original Research Shail BalaS. I. HarlapurAditya Kamalakar KanadeM. P. PotdarGurupada B. BalolV. B. KuligodLingareddy Usha Rani https://www.frontiersin.org/articles/10.3389/frai.2026.1904923 https://www.frontiersin.org/articles/10.3389/frai.2026.1904923 <![CDATA[The impact of artificial intelligence with multi-head self-attention based deep learning model for students' academic performance monitoring]]> 2026-09-03T00:00:00Z Original Research Mahmoud Ragab https://www.frontiersin.org/articles/10.3389/frai.2026.1920911 https://www.frontiersin.org/articles/10.3389/frai.2026.1920911 <![CDATA[AI-powered retinal imaging for early ASCVD risk stratification in a hybrid care model: preliminary findings from SAFER]]> 2026-09-03T00:00:00Z Original Research Hala ZakariaJuman AliMohammed Gouda IbrahimIdalys RomanAli HashemiIhsan Almarzooqi https://www.frontiersin.org/articles/10.3389/frai.2026.1832131 https://www.frontiersin.org/articles/10.3389/frai.2026.1832131 <![CDATA[Advanced security framework for 5G-SDN attack detection and mitigation using Affinity Cohesive Clustering and Improved Chaotic Walrus-Optimized Convolutional LSTM with Deep Dual Info-Varcoder]]> 2026-09-03T00:00:00Z Original Research Shameli R.Sujatha Rajkumar https://www.frontiersin.org/articles/10.3389/frai.2026.1903342 https://www.frontiersin.org/articles/10.3389/frai.2026.1903342 <![CDATA[Multi-resolution perceiver-based residual attention for explainable fault diagnosis in power transmission systems]]> 2026-09-03T00:00:00Z Original Research Shantanu Dinesh WaniS. AbinayaS. AbiramiR. Priyadarshini https://www.frontiersin.org/articles/10.3389/frai.2026.1815425 https://www.frontiersin.org/articles/10.3389/frai.2026.1815425 <![CDATA[Words matter: measuring and titrating the communicative character of AI]]> 2026-09-03T00:00:00Z Original Research William C. Kouns https://www.frontiersin.org/articles/10.3389/frai.2026.1795751 https://www.frontiersin.org/articles/10.3389/frai.2026.1795751 <![CDATA[A severity-aware multi-task CNN-transformer framework for explainable diabetic foot ulcer triage and mobile deployment]]> 2026-09-03T00:00:00Z Original Research AbdullahMuhammad Ateeb AtherZulaikha FatimaJosé Luis Oropeza Rodríguez https://www.frontiersin.org/articles/10.3389/frai.2026.1886470 https://www.frontiersin.org/articles/10.3389/frai.2026.1886470 <![CDATA[A comparative evaluation of deep learning models for the classification of encoded splice-junction sequences]]> 2026-09-03T00:00:00Z Original Research Yogesh KumarInderpreet KaurNandini ModiPriya BhardwajJaeyoung ChoiMuhammad Fazal Ijaz https://www.frontiersin.org/articles/10.3389/frai.2026.1867313 https://www.frontiersin.org/articles/10.3389/frai.2026.1867313 <![CDATA[Robust vision transformer adaptation for UAV imagery]]> 2026-09-03T00:00:00Z Original Research Jagan MurugesanP. Helen VijithaL. Jai VinitaA. K. ParvathyP. N. Jeipratha https://www.frontiersin.org/articles/10.3389/frai.2026.1909141 https://www.frontiersin.org/articles/10.3389/frai.2026.1909141 <![CDATA[NEUROCOG-DysNet: a multi-scale cognitive feature engineering framework with stacking ensemble for improved dyslexia screening]]> 2026-09-02T00:00:00Z Original Research Sudha MMurali S https://www.frontiersin.org/articles/10.3389/frai.2026.1902018 https://www.frontiersin.org/articles/10.3389/frai.2026.1902018 <![CDATA[Evaluating hybrid quantum–classical learning for financial forecasting: a comparative study with classical machine learning models on BSE indices]]> 2026-09-02T00:00:00Z Review Rashmi NairPankaj ChandreShahin MakubhaiBhagyashree ShendkarDisha DeotaleVijaya Patil https://www.frontiersin.org/articles/10.3389/frai.2026.1961460 https://www.frontiersin.org/articles/10.3389/frai.2026.1961460 <![CDATA[Correction: The collaboration code: how humans and AI work together across millions of conversations on job tasks]]> 2026-09-02T00:00:00Z Correction Frontiers Production Office https://www.frontiersin.org/articles/10.3389/frai.2026.1898557 https://www.frontiersin.org/articles/10.3389/frai.2026.1898557 <![CDATA[Digital nudging in software development: a review and research agenda]]> 2026-09-01T00:00:00Z Review Ingo PribikAlexander Felfernig https://www.frontiersin.org/articles/10.3389/frai.2026.1891738 https://www.frontiersin.org/articles/10.3389/frai.2026.1891738 <![CDATA[Synthetic resonance: a framework for growth-oriented human–AI relationships]]> 2026-09-01T00:00:00Z Conceptual Analysis Richard A. Fabes https://www.frontiersin.org/articles/10.3389/frai.2026.1847409 https://www.frontiersin.org/articles/10.3389/frai.2026.1847409 <![CDATA[Asymmetric uncertainty regulation in human–artificial intelligence interaction]]> 2026-09-01T00:00:00Z Original Research Karim C. AbbaspourArio Saeid VaghefiAbbas MirmashhouriNazanin AbbaspourMarkus LeippoldMory Ghomshei https://www.frontiersin.org/articles/10.3389/frai.2026.1849571 https://www.frontiersin.org/articles/10.3389/frai.2026.1849571 <![CDATA[NeuroPlast: a learnable activation function evaluated under knowledge distillation for medical image classification]]> 2026-08-31T00:00:00Z Original Research Fabrice VaussenatAbhiroop BhattacharyaJulie PayetteThibaut DesmettreAlireza SaidiGhyslain GagnonSylvain G. Cloutier