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The Transparency Revolution in Geohazard Science:A Systematic Review and Research Roadmap for Explainable Artificial Intelligence 认领 引用
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作者 Moein Tosan Vahid Nourani +5 位作者 Ozgur Kisi Yongqiang Zhang Sameh A.Kantoush Mekonnen Gebremichael Ruhollah Taghizadeh-Mehrjardi Jinhui Jeanne Huang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期77-117,共41页
The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unatt... The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics. 展开更多
关键词 Explainable artificial intelligence(XAI) geohazard assessment machine learning SHAP trustworthy AI model interpretability
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Narrative review of artificial intelligence(AI)in neuro-ophthalmic education:approaches for AI literacy 认领 引用
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作者 Ritu Sampige Tony Succar +3 位作者 Yena Jang Zain Moin Ahmed Abdou Eduardo Mayorga 《Annals of Eye Science》 2026年第1期40-50,共11页
Background and Objective:Artificial intelligence(AI)-based tools in graduate medical education are increasingly utilized to support and augment learning due to their ability to provide tailored feedback to learners.Wi... Background and Objective:Artificial intelligence(AI)-based tools in graduate medical education are increasingly utilized to support and augment learning due to their ability to provide tailored feedback to learners.With the growing use of AI in educational settings,there is a need to further understand the implications and utility of AI-based tools,particularly in the field of neuro-ophthalmology.With the increasing use of AI tools in neuro-ophthalmology,this narrative review provides an overview of the current and future use of AI for neuro-ophthalmic education.Methods:Two databases,PubMed and Google Scholar,were searched with the following keywords:Neuro-ophthalmology,Ophthalmology,Artificial Intelligence,Large Language Models,Deep Learning Models,Education,Medical Education,Teaching,Curriculum,Training,Diagnostics,and Diagnosis from May to October 2025.Inclusion criteria included peer-reviewed sources that were specific to AI and neuroophthalmology in the English language through 2025.Key Content and Findings:AI tools,including large language models(LLMs),deep learning models,and virtual reality(VR)devices,may be utilized to teach learners about creating a differential diagnosis,identifying diseases through imaging and diagnostics[e.g.,fundus photography,optical coherence tomography(OCT),visual field patterns,radiology images,and understanding exam preparation questions].Learners across various levels(i.e.,medical students,residents,fellows)may interact with custom generative pre-training transformers(GPTs)in real-time to delve deeper into ophthalmic conditions and receive personalized feedback based on each learner’s knowledge gaps.Additionally,AI tools may harness the power of active learning methods through case generation and Socratic questioning.However,the caveat is that such tools vary in their knowledge base of neuro-ophthalmology concepts.Nonetheless,learners need to acknowledge the current limitations in the knowledge base of AI data sets and the need for human surveillance of AI-generated neuro-ophthalmic output,particularly through hybrid AI educational models.Conclusions:With the current shortage of neuro-ophthalmologists and the projected shortage of ophthalmologists overall,AI tools may serve as accessible,efficient,individualized,and accurate educational resources for learners across training levels,including in resource-limited regions. 展开更多
关键词 Artificial intelligence(AI) large language models(LLMs) neuro-ophthalmology
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Artificial intelligence-based prediction of shear modulus and damping ratio of recycled tire rubber-soil mixtures for sustainable engineering applications 认领 引用
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作者 Ahmed Yar Akhtar Hing-Ho Tsang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期2158-2176,共19页
Frugal innovation stands as an imperative cog in the wheel of sustainable development.In the pursuit of simplicity,cost-effectiveness,and environmental compatibility,waste tire rubber and polyurethane-coated rubber(PU... Frugal innovation stands as an imperative cog in the wheel of sustainable development.In the pursuit of simplicity,cost-effectiveness,and environmental compatibility,waste tire rubber and polyurethane-coated rubber(PUcR)emerge as pivotal components in sustainable practices.These materials are advocated for various purposes,including protecting utility tunnels,serving as railway subgrades,and enhancing structural resilience through geotechnical seismic isolation(GSI).Their inherent characteristics,such as modest shear modulus(G)and robust damping ratio(D),make them well-suited for such endeavors,contributing to sustainability goals by repurposing substantial quantities of non-biodegradable waste.For practicality,leveraging artificial intelligence(AI)-based modern computing techniques for recycled material applications is imperative.In this regard,gene expression programming(GEP)was utilized to develop models for predicting the G and D of rubber–soil mixtures(RSMs)and polyurethane-coated RSMs(PUcRSMs).Employing laboratory testing data from 63 samples across three soil types,the newly proposed models demonstrated exceptional accuracy,with correlation coefficient(R2)values of 0.91 and 0.97 for G-prediction of RSM and PUcRSM,and 0.9 and 0.86 for D-prediction,respectively.Using AI-based methods,such as GEP to predict mixtures’dynamic response can cut laboratory costs and optimize mix designs,thereby advancing sustainable material applications. 展开更多
关键词 Frugal innovation Artificial intelligence(AI) Sustainability Gene expression programming(GEP) Recycled waste tire rubber Geotechnical seismic isolation(GSI)
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A comprehensive survey of artificial intelligence applications in UAV-enabled wireless networks 认领 引用 被引量：2
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作者 Li Zhou Hao Yin +3 位作者 Haitao Zhao Jibo Wei Dewen Hu Victor C.M.Leung 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期561-583,共23页
This comprehensive survey paper examines the applications of Artificial Intelligence(AI)in Unmanned Aerial Vehicle(UAV)-enabled wireless networks.With the increasing demand for efficient and adaptive communication sys... This comprehensive survey paper examines the applications of Artificial Intelligence(AI)in Unmanned Aerial Vehicle(UAV)-enabled wireless networks.With the increasing demand for efficient and adaptive communication systems,the integration of AI with UAV networks promises to revolutionize various aspects of wireless communication.The paper first outlines the background and motivation behind AI integration,highlighting the potential for enhanced network performance,autonomy,and adaptability.It then delves into the key AI applications across different network layers,including data sensing and collection,placement and trajectory optimization,radio resource management,routing and topology control,edge computing and caching,as well as security and privacy enhancement.For each application,the paper discusses relevant AI techniques,main findings,optimization objects,and the potential benefits and challenges.The survey also identifies open issues,such as the practical implementation gap,standardization issues,and real-world application barriers,and proposes future directions to address these challenges and further advance the field.In conclusion,the integration of AI with UAV-enabled Wireless Networks(UWNs)holds tremendous potential for transforming wireless communication,enabling new applications and services with unprecedented capabilities. 展开更多
关键词 Artificial intelligence(AI) Machine learning(ML) Unmanned aerial vehicle(UAV) Wireless network
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A Chinese Expert Consensus on the Artificial Intelligence Proficiency of Medical Students:Competencies and the Multi-Modal Assessment 认领 引用 被引量：1
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作者 Mengchun Gong Jiao Li +8 位作者 Yonghui Ma Bo Jin Wei Chen Yan Hou Li Hong Tianwen Lai Bohan Zhang Ge Wu Zhirong Zeng 《Health Care Science》 CSCD 2026年第1期49-57,共9页
Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for me... Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements. 展开更多
关键词 AI proficiency artificial intelligence(AI) assessment competency framework medical education
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Artificial intelligence in urological malignancy diagnosis and prognosis:current status and future prospects 认领 引用
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作者 Mingwei Zhan Zhaokai Zhou +10 位作者 Jianpeng Zhang XinWang Canxuan Li Bochen Pan Zhanyang Luo Wenjie Shi Yongjie Wang Minglun Li Weizhuo Wang Run Shi Jingyu Zhu 《The Canadian Journal of Urology》 SCIE 2026年第1期35-49,共15页
Artificial intelligence(AI)is transforming the diagnostic landscape of malignant tumors in the urinary system,including prostate cancer,bladder cancer,and renal cell carcinoma(RCC).By integrating imaging,pathology,and... Artificial intelligence(AI)is transforming the diagnostic landscape of malignant tumors in the urinary system,including prostate cancer,bladder cancer,and renal cell carcinoma(RCC).By integrating imaging,pathology,and molecular data,AI enhances the precision and reproducibility of tumor detection,grading,and risk stratification.In prostate cancer,AI-assisted multiparametric Magnetic resonance imaging(MRI)and digital pathology systems improve lesion localization and Gleason scoring.For bladder cancer,deep learning-based cystoscopy and radiomics models from Computed tomography/magnetic resonance imaging(CT/MRI)enable real-time lesion segmentation and non-invasive biomarker prediction,such as Programmed Cell Death-Ligand 1(PD-L1)expression.In RCC,AI,combined with CT/MRI and multi-omics data,aids in subtype classification and prognostic prediction,supporting personalized therapy.However,despite these promising advances,challenges such as data standardization,model generalizability,interpretability,and regulatory compliance hinder AI’s clinical translation.This review outlines the current state of AI in urological cancer diagnosis and prognosis,its technological innovations,and the clinical challenges and opportunities that lie ahead. 展开更多
关键词 Artificial intelligence urologic cancers prostate cancer bladder cancer renal cell carcinoma multimodal AI
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PRIME:an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer 认领 引用
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作者 Yu Wang Yong-Bo Xiang +7 位作者 Xiao-Wei Chen Tao Zhang Jian-Yang Wang Wen-Yang Liu Lei Deng Lu-Hua Wang Shu-Geng Gao Nan Bi 《Military Medical Research》 SCIE CAS CSCD 2026年第5期747-765,共19页
Background:Despite the predictive impact of circulating tumor DNA(ctDNA)minimal residual disease(MRD),accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lu... Background:Despite the predictive impact of circulating tumor DNA(ctDNA)minimal residual disease(MRD),accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer(NSCLC)patients to guide personalized therapy remains challenging.This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources.Methods:Liquid biopsy data,blood-based genomic alterations,clinicopathological features,and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from six cohorts.PRIME(Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD,Mutations,and clinical-therapeutic features)was trained by 6 machine learning algorithms across four cohorts and validated in two independent cohorts.Model performance was evaluated by the area under the curve(AUC)and interpreted by SHapley Additive exPlanations(SHAP).Whole-exome sequencing(WES)or whole-genome sequencing(WGS)of tumor tissue from 430 stageⅡ-ⅢNSCLC patients and RNA-sequencing(RNA-seq)data from 1149 subjects,sourced from The Cancer Genome Atlas,were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms.Results:A global dataset encompassing 781 blood samples from 493 patients was analyzed.Clinical stage,pretreatment ctDNA,post-treatment MRD,blood-based Kelch-like ECH-associated protein 1(KEAP1),serinehreonine kinase 11(STK11),and cyclin-dependent kinase inhibitor 2A(CDKN2A)mutations,and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training.WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1,STK11,and CDKN2A mutations,which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity.The neural network(NN)model exhibited optimal prediction of treatment failure risk in the training(AUC=0.85,95%CI 0.81-0.89)and validation sets(AUC=0.82,95%CI 0.74-0.89).SHAP analysis indicated that MRD(+0.306),treatment modality(+0.128),and pre-treatment ctDNA(+0.043)ranked in the top 3 contributions.NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures,and demonstrated consistent robustness across different clinical scenarios.High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery.Conclusions:As an interpretable model integrating readily-accessible and crucial clinical-genomic predictors,PRIME achieves enhanced performance,allowing for early outcome prediction,refined risk stratification,and personalized clinical decision-making. 展开更多
关键词 Non-small cell lung cancer Artificial intelligence(AI) Liquid biopsy Machine learning(ML) Circulating tumor DNA Minimal residual disease
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AI Empowers Supply Chain Intelligence:A Three-Chain Four-Intelligence Framework 认领 引用
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作者 Zuo-Jun Max Shen Shaochong Lin 《Engineering》 SCIE EI CSCD 2026年第6期430-443,共14页
This study introduces a novel conceptual framework to understand the transformative impact of artificial intelligence(AI)on global supply chains.We propose a Three-Chain Four-Intelligence framework that systematically... This study introduces a novel conceptual framework to understand the transformative impact of artificial intelligence(AI)on global supply chains.We propose a Three-Chain Four-Intelligence framework that systematically analyzes how AI reconfigures supply chain architecture and capabilities through enhanced contextual awareness.The Three-Chain perspective examines how AI transforms the logistics chain(physical flow),information chain(data flow),and value chain(value creation)from fragmented opera-tions to synchronized intelligent ecosystems.The Four-Intelligence pathway maps the evolutionary pro-gression from digital connectivity to operational optimization,collaborative ecosystems,and ultimately self-evolving intelligent systems.AI serves as an orchestrating force that processes rich contextual infor-mation spanning product attributes,market dynamics,environmental conditions,and operational reali-ties.We demonstrated the practical application of the framework through a comprehensive case study of JD.com,where AI implementation across all dimensions yielded quantifiable improvements.Our analysis reveals that the most transformative supply chain advancements emerge at the intersection of multiple chains with increasingly sophisticated contextual awareness.The paper concludes by identifying six emerging research frontiers,such as generative AI integration with decision optimization. 展开更多
关键词 Artificial intelligence Supply chain intelligence Machine learning Intelligent decision-making Generative AI
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Integrating artificial intelligence with human reasoning in oncology:questions on real-world implementation and patient-centric evidence 认领 引用
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作者 Carlos M.Ardila Anny Marcela Vivares-Builes Eliana Pineda-Vélez 《Military Medical Research》 SCIE CAS CSCD 2026年第3期515-516,共2页
The article by Jiang et al.[1],"Leveraging artificial intelligence for clinical decision support in personalized standard regimen recommendation for cancer"published in Military Medical Research,addresses a ... The article by Jiang et al.[1],"Leveraging artificial intelligence for clinical decision support in personalized standard regimen recommendation for cancer"published in Military Medical Research,addresses a pivotal issue in contemporary oncology:how artificial intelligence(AI)can augment clinical reasoning to refine regimen selection.Their discussion of data learnability,model usability,and the envisioned SINGULARITY framework reflects a forward-looking approach to precision medicine. 展开更多
关键词 Artificial intelligence(AI) Precision medicine Standard of care
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Balancing global standards and regional nuances in breast cancer care: the role of guidelines, clinical research, precision medicine, and artificial intelligence in advancing quality of care for patients worldwide 认领 引用
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作者 Michael Gnant 《Cancer Biology & Medicine》 SCIE CAS CSCD 2026年第3期314-319,共6页
Breast cancer remains a global health challenge with greater than 2.3 million new cases diagnosed annually 1,according to the World Health Organization1.Management of breast cancer is shaped by a complex interplay of ... Breast cancer remains a global health challenge with greater than 2.3 million new cases diagnosed annually 1,according to the World Health Organization1.Management of breast cancer is shaped by a complex interplay of international guidelines,regional adaptations,and the rapidly evolving fields of precision medicine and artificial intelligence(AI). 展开更多
关键词 breast cancer care regional nuances clinical research guidelines artificial intelligence ai precision medicine breast cancer global standards
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The Development of Artificial Intelligence:Toward Consistency in the Logical Structures of Datasets,AI Models,Model Building,and Hardware? 认领 引用
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作者 Li Guo Jinghai Li 《Engineering》 SCIE EI CSCD 2025年第7期13-17,共5页
The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficu... The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration. 展开更多
关键词 consistency datasets model building ai models artificial intelligence ai explore potential directions hardware artificial intelligence
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Multimodal artificial intelligence predicts PIK3CA mutation in breast cancer from digital pathology and clinical data:a multicenter study 认领 引用
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作者 Jiaxian Miao Qi Liu +11 位作者 Jianing Zhao Shishun Fan Shenwen Wang Feng Ye Si Wu Jinze Li Huirui Zhang Meng Zhang Hong Bu Xiao Han Lianghong Teng Yueping Liu 《Cancer Biology & Medicine》 SCIE CAS CSCD 2026年第3期430-450,共21页
Objective:Accurate detection of PIK3CA mutations is essential for guiding PI3K-targeted therapies in breast cancer,yet sequencing is not universally accessible,and single-modality prediction models have limited perfor... Objective:Accurate detection of PIK3CA mutations is essential for guiding PI3K-targeted therapies in breast cancer,yet sequencing is not universally accessible,and single-modality prediction models have limited performance.This study developed a multimodal deep learning framework integrating whole-slide imaging(WSI)and structured clinical data to improve mutation prediction.Methods:A total of 1,047 patients from TCGA and 166 patients from 3 external centers were included.The histopathology model used a transformer-based pretrained encoder(H-optimus-0)and a clustering-constrained attention multiple instance learning(CLAM-SB MIL)classifier to generate WSI-level representations.The clinical model incorporated engineered clinical variables and an extreme gradient boosting(XGBoost)model.A decision-level late fusion strategy(Multimodal PIK3CA Model,MPM)combined probabilistic outputs from both branches.Performance was evaluated with the area under the curve(AUC)and secondary metrics.Interpretability was assessed via attention heatmaps and shapley additive explanations(SHAP)analysis.Results:MPM outperformed single-modality models.It achieved an AUC of 0.745 on TCGA and maintained stable performance across external cohorts(0.695,0.690,and 0.680).SHAP analysis identified molecular subtype as the most influential clinical feature,whereas attention maps highlighted mutation-associated morphological regions.Conclusions:The developed multimodal framework effectively integrates complementary morphological and clinical information,and provides a robust and generalizable method for predicting PIK3CA mutation status.Strong multicenter adaptability and biological interpretability support its potential use as a clinical decision-support tool and an accessible alternative to molecular testing. 展开更多
关键词 Breast cancer PIK3CA mutation multimodal artificial intelligence whole-slide imaging computational pathology
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CARE framework:Rethinking randomized controlled trials for medical artificial intelligence 认领 引用
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作者 Sicheng Yang Qiuxia Yang +1 位作者 Yize Mao Lei Zhu 《Chinese Journal of Cancer Research》 SCIE CAS CSCD 2026年第2期197-199,共3页
Randomized controlled trials(RCTs)are the important methods for evaluating medical treatments(1,2).Currently,traditional RCTs are not fully applicable to medical artificial intelligence(AI).AI systems produce results ... Randomized controlled trials(RCTs)are the important methods for evaluating medical treatments(1,2).Currently,traditional RCTs are not fully applicable to medical artificial intelligence(AI).AI systems produce results that are inherently probabilistic,and their performance can vary across different settings.They are highly sensitive to changes in deployment environments,patient population characteristics,clinical workflows,and continuous software updates(1).To bridge this gap,we propose the CARE framework:Clinical endpoint redefinition,Adversarial safety testing,and Real-world Environment adaptability.This provides a new way to design RCTs tailored to the unique characteristics of medical AI. 展开更多
关键词 care framework clin artificial intelligence ai ai randomized controlled trials rcts adversarial safety testing evaluating medical treatments currentlytraditional medical artificial intelligence real world environment adaptability clinical endpoint redefinition
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Interaction of artificial intelligence,mental disorders,and diverse data modalities:Potential treatment management based on the"method-disease-data"axis 认领 引用
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作者 Xu Tian Ning Wang +2 位作者 Jin Yan Yiming Chen Ke Ma 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第9期3919-3932,共14页
Although many previous studies have highlighted the advances in prediction models,instruments for pathological and histological diagnosis and treatment,as well as individualized treatment modalities in mental disorder... Although many previous studies have highlighted the advances in prediction models,instruments for pathological and histological diagnosis and treatment,as well as individualized treatment modalities in mental disorders,these previous syntheses usually study the research outcomes separately and ignore the holistic integration of research regarding artificial intelligence technological approaches,data sets used and applications in mental health research.We used the BioBERT pretrained language model to systematically extract relevant information and develop an extensive knowledge graph that includes 3158 entities connected with 3248 different relationships.Our knowledge graph delineates essential artificial intelligence technological frameworks and explicitly maps out the relationships linking artificial intelligence methods,mental disorders,and diverse data modalities.The synthesis,centered on the analytical axis of“method-disease-data,”highlights key research areas where artificial intelligence and neuropsychiatry meet.Specifically,it focuses on key applications in early detection,improved accuracy of diagnosis,and individualized therapeutic interventions.In addition,we summarized the applications derived from basic research findings that extend to ongoing clinical trials,revealing the path toward future clinical application in psychiatric work.Importantly,the research paid special attention to the application of artificial intelligence in identifying key brain regions and neural circuits,providing important clues for elucidating the neural mechanisms of mental disorders and developing targeted interventions.Although artificial intelligence presents great opportunities,there are also significant challenges,including imbalanced data sets,ethical issues,and clinical concerns about trustworthiness and transparency.Strategies to address these challenges are proposed,and a perspective on emerging methods enabled by artificial intelligence is provided,which are expected to greatly change the management and treatment of the future. 展开更多
关键词 artificial intelligence clinical decision support clinical trial computational psychiatry early diagnosis knowledge discovery personalized medicine precision psychiatry psychiatric disorders treatment prediction
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Lightweight AI-Powered Intrusion Detection via Edge Computing 认领 引用
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作者 Jackson Diaz-Gorrin Candido Caballero-Gil +1 位作者 Pino Caballero-Gil Joanna Kolodziej 《Computers, Materials & Continua》 SCIE EI 2026年第9期1383-1406,共24页
A lightweight flow-based intrusion detection system is proposed for identifying Mirai-based distributed denial-of-service attacks in Internet of Things(IoT)environments.Efficient intrusion detection at the network edg... A lightweight flow-based intrusion detection system is proposed for identifying Mirai-based distributed denial-of-service attacks in Internet of Things(IoT)environments.Efficient intrusion detection at the network edge is essential for resource-constrained IoT deployments,where devices operate with limited processing,memory,and energy resources,making centralized or computationally intensive solutions impractical in real-world scenarios.Network traffic is represented using statistical and temporal features extracted from unidirectional flows constructed from the TII-SSRC-23 dataset.A balanced subset of 10,000 samples is used for training and evaluation,ensuring balanced data distribution and improving generalization across different traffic conditions.Three machine learning models,a multilayer perceptron,a support vector machine,and LightGBM,are investigated to evaluate trade-offs between detection performance,complexity,and suitability for deployment in resource-constrained edge environments.Experimental results show that LightGBM achieves the best performance,obtaining an accuracy of 0.99,an F1-score of 1.00,and an AUC of 1.00,while consistently maintaining a low false positive rate.The selected model is deployed on the NVIDIA Jetson Orin Nano platform for real-time inference under resource constraints and evaluated for continuous operational performance.The system operates with low latency and reduced memory and computational requirements,making it highly suitable for edge IoT security scenarios. 展开更多
关键词 Cybersecurity intrusion detection machine learning internet of things edge computing artificial intelligence
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Slope rockbolting using key block theory:Force transfer and artificial intelligence-assisted multi-objective optimisation 认领 引用
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作者 Jessica Ka Yi Chiu Charlie Chunlin Li +1 位作者 Ole Jakob Mengshoel Vidar Kveldsvik 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期73-91,共19页
This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material u... This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material usage,and costs.In the first stage,an extended key block analysis identifies key blocks and key block groups,accounting for progressive failure and force interactions.The second stage uses AI algorithms to optimise rockbolting design,balancing stability,cost,and material use.The most efficient algorithms include the multi-objective tree-structured Parzen estimator(MOTPE)and non-dominated sorting genetic algorithms(NSGA-II and NSGA-III).Applied to the Larvik rock slope,the optimised solution uses 18 pre-tensioned cablebolts,providing 13.2 MN of active force and achieving a factor of safety of 1.31 while reducing the average anchorage length by approximately 16%compared to traditional design.The AI-assisted approach also reduces computation time by over 90%compared to Quasi-Monte Carlo(QMC)methods,demonstrating its efficiency for small-scale civil engineering projects and large-scale mining operations.The developed tool is practical,compatible with Building Information Modelling(BIM),and ready for engineering implementation,supporting sustainable and cost-effective rock slope stabilisation.While the method is largely automated,professional judgement remains crucial for verifying ground conditions and selecting the final solution.Future work will focus on integrating data uncertainties,addressing complex block deformation mechanisms,refining optimisation objectives,and improving the performance of multi-objective optimisation for slope rockboling applications to further enhance the method's versatility. 展开更多
关键词 Rock anchoring Slope stability 3D modelling Key block Parametric design Bio-inspired artificial intelligence(AI)
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Artificial intelligence and major depression:Toward mechanistic and clinically actionable models 认领 引用
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作者 Filiz Ozsoy Gulay Tasci +2 位作者 Burak Tasci Sengul Dogan Turker Tuncer 《World Journal of Psychiatry》 SCIE 2026年第7期41-57,共17页
Major depressive disorder is a widespread psychiatric disorder driven by complex genetic,neurobiological,psychological,and environmental mechanisms.Conventional diagnostic systems,such as the Diagnostic and Statistica... Major depressive disorder is a widespread psychiatric disorder driven by complex genetic,neurobiological,psychological,and environmental mechanisms.Conventional diagnostic systems,such as the Diagnostic and Statistical Manual of Mental Disorders,the Fifth Edition and International Classification of Diseases,rely on symptom-based evaluations,which are limited by subjectivity,symptom overlap,and restricted applicability across diverse populations.Advances in artificial intelligence(AI)provide new opportunities for objective,data-driven depression assessment.This review synthesizes epidemiological,etiological,and clinical evidence to evaluate AI-based approaches for depression detection and characterization.Machine learning,deep learning,and large language modelbased methods applied to multimodal data,including electronic health records,neuroimaging,electroencephalography(EEG),speech and language data,and digital behavioral signals,were systematically examined,with particular attention to interpretability and ethical considerations.Depression was consistently associated with monoaminergic and neurotrophic dysregulation,inflammation,hypothalamic pituitary adrenal axis dysfunction,and frontolimbic network abnormalities.AI models demonstrated strong discriminative performance using biological and behavioral markers,particularly when multimodal data integration was employed.Neuroimaging and EEG analyses revealed network-level alterations,while natural language processing approaches captured linguistic and acoustic markers linked to symptom severity and suicide risk.AI-based systems have substantial potential to advance precision psychiatry by enabling earlier detection and personalized treatment of depression.However,challenges,including dataset bias,methodological heterogeneity,limited interpretability,and insufficient real-world validation,must be addressed through standardized,transparent,and ethically guided clinical research. 展开更多
关键词 Major depressive disorder Artificial intelligence Computational psychiatry Machine learning Multimodal data integration Precision psychiatry
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Artificial intelligence in digital pathology diagnosis and analysis:technologies,challenges,and future prospects 认领 引用
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作者 Xiu-Ming Zhang Tian-Hong Gao +27 位作者 Qiu-Yu Cai Jia-Bin Xia Yu-Ning Sun Jian Yang Wei-Han Li Sheng-Xu-Ming Zhang Heng-Rui Lou Xiao-Tian Yu Kai-Wen Hu Jing-Wen Ye Jin-Xing Zhang Jie Lei Le-Chao Cheng Lin-Jie Xu Qing Chen He-Xiang Wang Mei-Fu Gan Cheng Lu Nan Pu Ming-Li Song Xin Chen Wen-Jie Liang Han Lv Chao-Qing Xu Zai-Yi Liu Jing Zhang Kai Yan Zun-Lei Feng 《Military Medical Research》 SCIE CAS CSCD 2026年第6期991-1029,共39页
Artificial intelligence(AI)offers transformative potential in pathology,where histopathological images remain the diagnostic gold standard due to their rich morphological and molecular information.While the rapid deve... Artificial intelligence(AI)offers transformative potential in pathology,where histopathological images remain the diagnostic gold standard due to their rich morphological and molecular information.While the rapid development of AI-driven computational pathology tools is revolutionizing disease interpretation,these technologies have not yet been systematically evaluated.Therefore,this review systematically evaluates AI applications across the diagnostic continuum,from image preprocessing and tumor classification to prognostic stratification and the discovery of predictive biomarkers.It presents a technical taxonomy of the algorithms and foundation models powering these applications,benchmarking their performance across diverse diagnostic tasks through rigorous comparative analyses.It also identifies critical challenges in clinical translation,including computational scaling,noisy annotations,interpretability gaps,and domain shifts.Finally,it proposes a roadmap for advancing AI applications in precision oncology and pathological research.By bridging technological innovation with clinical needs,this review aims to accelerate the integration of robust,unified,scalable AI solutions into diagnostic workflows. 展开更多
关键词 Artificial intelligence(AI) Pathology images Quantitative feature Pathology foundation model
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Clinical Application Progress of Artificial Intelligence in Pancreatic Cancer:From Diagnosis to Immunotherapy 认领 引用
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作者 Zehao Wei Xuejian Liu +2 位作者 Zheng Zhang Yimin Ma Min Xu 《Oncology Research》 SCIE 2026年第7期305-320,共16页
Pancreatic cancer is one of the most lethal malignancies,characterized by difficulties in early diagnosis,limited therapeutic options,and generally poor patient prognosis.In recent years,immunotherapy has provided new... Pancreatic cancer is one of the most lethal malignancies,characterized by difficulties in early diagnosis,limited therapeutic options,and generally poor patient prognosis.In recent years,immunotherapy has provided new opportunities for the treatment of pancreatic cancer;however,its clinical efficacy has been substantially constrained by the complex tumor microenvironment(TME)and immune evasion mechanisms.With the rapid advancement of artificial intelligence(AI)technologies,AI has demonstrated great potential in the early detection of pancreatic cancer,prediction of immunotherapeutic responses,and design of personalized treatment strategies.This review systematically summarizes the latest advances in the application of artificial intelligence in pancreatic cancer immunotherapy,with a particular focus on key AI assisted technologies,including tumor immune microenvironment characterization,prediction of genetic mutation profiles,nanomedicine design,and dynamic monitoring of therapeutic responses.By integrating single cell sequencing and multi-omics data analyses,we discuss how AI can effectively address critical bottlenecks in immunotherapy.In addition,this article analyzes current technical challenges and future development trends,aiming to provide a theoretical foundation and practical guidance for achieving precision immunotherapy in pancreatic cancer and to promote clinical translation and application in this field. 展开更多
关键词 Pancreatic cancer artificial intelligence(AI) immunotherapy tumor microenvironment(TME)
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The serendipity paradox of artificial intelligence in oncology 认领 引用
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作者 Zejia Mao Bo Xu 《Intelligent Oncology》 CAS 2026年第3期1-3,共3页
Artificial intelligence(AI)in oncology is often designed to exploit known patterns,creating a“serendipity paradox”:reliance on supervised learning and average performance systematically filters out the rare,anomalou... Artificial intelligence(AI)in oncology is often designed to exploit known patterns,creating a“serendipity paradox”:reliance on supervised learning and average performance systematically filters out the rare,anomalous,and unclassifiable.This editorial dissects three mechanisms driving this loss of surprise and proposes actionable strategies:anomaly detection as a primary objective,uncertainty-aware human-AI interfaces,and noise-preserving data governance,to reorient AI toward discovery of the genuinely unknown. 展开更多
关键词 average performance actionable strategies anomaly detection supervised learning anomaly detection artificial intelligence ai artificial intelligence serendipity paradox oncology
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