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 2026-05-24T17:44:48.802+00:00 60 https://www.frontiersin.org/articles/10.3389/frai.2026.1808422 https://www.frontiersin.org/articles/10.3389/frai.2026.1808422 <![CDATA[MRI-based morphometric analysis of the patellofemoral joint: diagnostic modeling of knee pathologies in adolescents]]> 2026-05-22T00:00:00Z Original Research Dusan SpasicGoran DjuricicJelena Djokic KovacBojan BukvaVladimir RadlovicMilos MaleticStanislav RajkovicMarko Radulović https://www.frontiersin.org/articles/10.3389/frai.2026.1797587 https://www.frontiersin.org/articles/10.3389/frai.2026.1797587 <![CDATA[Neuro-symbolic NLP: taxonomy, assessment, and directions]]> 2026-05-22T00:00:00Z Systematic Review Stergios ChatzikyriakidisShalom Lappin https://www.frontiersin.org/articles/10.3389/frai.2026.1759758 https://www.frontiersin.org/articles/10.3389/frai.2026.1759758 <![CDATA[An open-source optimization model for sustainable open-pit mine production scheduling]]> 2026-05-21T00:00:00Z Original Research Justina Senam LotsuGilbert Yaw BimpongKwaku Boakye https://www.frontiersin.org/articles/10.3389/frai.2026.1794125 https://www.frontiersin.org/articles/10.3389/frai.2026.1794125 <![CDATA[KG-HiAttention: synergizing AI-based knowledge graphs and deep learning for explainable software vulnerability analysis]]> 2026-05-21T00:00:00Z Original Research Francisco Pinto-SantosCarolina ZatoHéctor QuintiánTian Cheng LiPablo Chamoso https://www.frontiersin.org/articles/10.3389/frai.2026.1831527 https://www.frontiersin.org/articles/10.3389/frai.2026.1831527 <![CDATA[Malus’s law-enhanced Michelson interferometer with PSO-based calibration and data-driven error compensation for medical micro-displacement measurement]]> 2026-05-21T00:00:00Z Original Research Yibo WangYuetong LvLuyang XieShilian Dong https://www.frontiersin.org/articles/10.3389/frai.2026.1825365 https://www.frontiersin.org/articles/10.3389/frai.2026.1825365 <![CDATA[Protocol-constrained AI enhances tacrolimus dosing accuracy in kidney transplant care]]> 2026-05-21T00:00:00Z Brief Research Report Benjamin BizerOscar A. Garcia ValenciaFlora KincsesJose Arriola-MontenegroCharat ThongprayoonJing MiaoWisit Cheungpasitporn https://www.frontiersin.org/articles/10.3389/frai.2026.1751832 https://www.frontiersin.org/articles/10.3389/frai.2026.1751832 <![CDATA[Perceived benefits and constraints of artificial intelligence integration in teachers’ college libraries: a TAM-based study]]> 2026-05-21T00:00:00Z Original Research Inos ChibidiLogic Magwa https://www.frontiersin.org/articles/10.3389/frai.2026.1836120 https://www.frontiersin.org/articles/10.3389/frai.2026.1836120 <![CDATA[Dynamic coherence windows: a multi-scale structural coherence measurement framework for AI systems]]> 2026-05-21T00:00:00Z Original Research Christian St-Louis https://www.frontiersin.org/articles/10.3389/frai.2026.1827586 https://www.frontiersin.org/articles/10.3389/frai.2026.1827586 <![CDATA[Evaluating AI adoption challenges in healthcare using a Multi-Criteria Decision-Making approach: implications for predictive risk analytics]]> 2026-05-20T00:00:00Z Original Research Pulidindi VenugopalPratibha GargChand PrakashSunil KumarNeha Gupta https://www.frontiersin.org/articles/10.3389/frai.2026.1803863 https://www.frontiersin.org/articles/10.3389/frai.2026.1803863 <![CDATA[Hybrid machine learning forecasting for resilient and sustainable pharmaceutical supply chains under regulatory and seasonal disruption]]> 2026-05-20T00:00:00Z Original Research Khalid YahyaMehdi SafaeiSaleh Al Dawsari https://www.frontiersin.org/articles/10.3389/frai.2026.1844338 https://www.frontiersin.org/articles/10.3389/frai.2026.1844338 <![CDATA[A vision language model for generating XML-based organ-level plant architecture representations of cowpea from simulated images]]> 2026-05-20T00:00:00Z Original Research Heesup YunIsaac Kazuo UyeharaIoannis DroutsasEarl RanarioChristine H. DiepenbrockBrian N. BaileyJ. Mason Earles https://www.frontiersin.org/articles/10.3389/frai.2026.1814362 https://www.frontiersin.org/articles/10.3389/frai.2026.1814362 <![CDATA[Image processing and AI techniques for climate change detection using remote sensing: a comprehensive review]]> 2026-05-20T00:00:00Z Review Anirudh AgarwalShreya KumarG. K. RajiniIndragandhi Vairavasundaram https://www.frontiersin.org/articles/10.3389/frai.2026.1828950 https://www.frontiersin.org/articles/10.3389/frai.2026.1828950 <![CDATA[A novel lightweight deep learning model for early prediction of cardiovascular disease]]> 2026-05-20T00:00:00Z Original Research Nasir GulAurangzeb KhanMazliham Mohd Su'udMuhammad Mansoor AlamDanyal Skandri https://www.frontiersin.org/articles/10.3389/frai.2026.1789644 https://www.frontiersin.org/articles/10.3389/frai.2026.1789644 <![CDATA[Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations]]> 2026-05-19T00:00:00Z Systematic Review Zuzanna StępnickaNatalia PiórkowskaMalwina BrożynaTomasz MatysAdam Junka https://www.frontiersin.org/articles/10.3389/frai.2026.1716935 https://www.frontiersin.org/articles/10.3389/frai.2026.1716935 <![CDATA[Artificial intelligence in carotid research: a 25-year bibliometric analysis of global trends and future directions]]> 2026-05-19T00:00:00Z Systematic Review Shi-Chao ZhuJia-Xin DongYu-Dan WuYue GaoRui GaoYong-Hui YuanShuang WangGuang-Wei Zhang https://www.frontiersin.org/articles/10.3389/frai.2026.1821975 https://www.frontiersin.org/articles/10.3389/frai.2026.1821975 <![CDATA[Exploring the determinants of anemia in young adult women (20–24 years): the role of AI-based interventions in health management]]> 2026-05-19T00:00:00Z Original Research Sireesha GuttapalamA. M. BeulahM. NiharikaC. PradeepthiD. Madhavi https://www.frontiersin.org/articles/10.3389/frai.2026.1776338 https://www.frontiersin.org/articles/10.3389/frai.2026.1776338 <![CDATA[The validity and reliability of a real-time AI-based neck exercise program among young adults in Thailand: evaluation of accuracy and execution time]]> 2026-05-19T00:00:00Z Original Research Yadanuch BoonyaratanaNacha ChondamrongkulVitsarut ButtagatPattanasin Areeudomwong https://www.frontiersin.org/articles/10.3389/frai.2026.1779276 https://www.frontiersin.org/articles/10.3389/frai.2026.1779276 <![CDATA[AI snake oil? A risk/benefit analysis for toxicology]]> 2026-05-19T00:00:00Z Hypothesis and Theory Thomas HartungMohan RaoMamta BehlAlexandra Maertens 3 × 104 peptide or chemical records and report performance ranging from modest in prospective screening settings to strong on narrower, well-curated endpoints. This manuscript presents a critical analysis of the dual nature of AI in toxicology. We review the state of the art in AI-enabled applications, ranging from Green Toxicology and the Human Exposome to specific challenges in safety assessment for biologics and synthetic peptides. Particular attention is given to the gap between rapid model development and regulatory acceptance, highlighted by the challenges of model interpretability, dataset bias, insufficient external validation, and the assessment of complex endpoints like immunogenicity. To navigate these complexities, we discuss the next-generation “e-validation” framework and emphasize the TREAT principle - Trustworthiness, Reproducibility, Explainability, Applicability, and Transparency - as a foundation for building regulatory trust. We hypothesize that AI-based methods in toxicology can achieve regulatory acceptance when they satisfy the TREAT criteria and undergo continuous e-validation within a clearly defined context of use. This framework distinguishes credible AI applications from “snake oil” by establishing measurable criteria for trust-building, including dataset provenance, external validation, uncertainty characterization, and life-cycle monitoring. We argue that AI is neither a miracle cure nor a technological illusion, but a powerful evidence engine that can contribute to a more predictive and ethical toxicological science when it is rigorously validated.]]> https://www.frontiersin.org/articles/10.3389/frai.2026.1794183 https://www.frontiersin.org/articles/10.3389/frai.2026.1794183 <![CDATA[Deep learning classification of reproductive tissue from ultrasound: sex determination in red abalone (Haliotis rufescens)]]> 2026-05-18T00:00:00Z Original Research Edwin A. SolaresAnthony TongSohyun YooWelokiheiakeaeloa Gosline-NiheuKevin JacobGordon FelizSachin LoecherYuan ZhaiMaggie MahSara E. BolesAyodeji E. FagbohunJackson A. Gross https://www.frontiersin.org/articles/10.3389/frai.2026.1748985 https://www.frontiersin.org/articles/10.3389/frai.2026.1748985 <![CDATA[Artificial intelligence techniques for classification of Alzheimer's disease using neuroimaging data: a review]]> 2026-05-18T00:00:00Z Review Y. NibilaM. Sivagami