Hydrogen evolution was detected in an artificial system composed of light-harvesting unit of purified photosystem I, catalyst of hydrogenase, methyl viologen and electron donor under radiation. Absorption spectral fea...Hydrogen evolution was detected in an artificial system composed of light-harvesting unit of purified photosystem I, catalyst of hydrogenase, methyl viologen and electron donor under radiation. Absorption spectral features confirmed that electron transfer from electron donors to proton was via a photoinduced reductive process of methyl viologen.展开更多
Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and...Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.展开更多
As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds...As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems.However,its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm.To address these concerns,this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments,examining both their benefits and associated risks.A systematic literature review was conducted across major scientific databases,including IEEE,Elsevier,Springer Nature,ACM,MDPI,and Wiley,covering peer-reviewed journal and conference papers published between 2017 and 2026.Studies were selected based on predefined inclusion and exclusion criteria following a structured screening process.Based on an analysis of 101 selected studies,this survey categorizes artificial intelligence-based threat detection approaches across the physical,control,and application layers of industrial control systems and examines poisoning,evasion,and extraction attacks targeting industrial artificial intelligence.The findings identify key research trends,highlight unresolved security challenges,and discuss implications for the secure deployment of artificial intelligence-enabled cybersecurity solutions in industrial control systems.展开更多
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
Background:Artificial intelligence(AI)-assisted threedimensional(3D)surgical platforms,integrated with augmented reality,have the potential to improve intraoperative anatomical recognition and provide surgeons with an...Background:Artificial intelligence(AI)-assisted threedimensional(3D)surgical platforms,integrated with augmented reality,have the potential to improve intraoperative anatomical recognition and provide surgeons with an immersive,dynamic operating environment during urooncological procedures.This review aims to examine the current applications of AI in robotic uro-oncology,with a particular focus on its role in facilitating intraoperative navigation during complex surgeries.Methods:A systematic literature search was performed across PubMed,the National Library of Medicine,MEDLINE,the Cochrane Central Register of Controlled Trials(CENTRAL),ClinicalTrials.gov,and Google Scholar to identify relevant studies published up to July 2025.The search strategy incorporated a predefined set of keywords,including AI,machine learning,radical prostatectomy(RP),robotic-assisted radical prostatectomy(RARP),robotassisted partial nephrectomy(RAPN),and robot-assisted radical cystectomy(RARC).Only clinical trials,full-text peer-reviewed publications,and original research articles were included.Studies were eligible for inclusion if they evaluated or described applications of AI in RARP,RAPN,or RARC.Results:Technological advancements have substantially transformed the field of uro-oncologic surgery.In particular,AI and AI-assisted intraoperative navigation in RARP demonstrate considerable potential to objectively assess surgical performance and predict clinical outcomes.In RAPN,the adoption of preoperative,interactive 3D virtualmodels for surgical planning has influenced surgical decisions,thus,enhanced precision in resection planning correlates with superior nephron-sparing outcomes and optimized selective clamping.AI applications in RARC,techniques such as augmented reality(AR)can overlay critical information on the surgical field,by facilitating navigation through complex anatomical planes and enhancing identification of critical structures.Conclusion:AI appears to enhance robotic uro-oncologic procedures by increasing operative precision and supporting individualised surgical treatment strategies.展开更多
With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenu...With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.展开更多
The advancement of Artificial Intelligence(AI)has garnered significant attention within the academic research community,reflecting the prevailing zeitgeist.However,there is a paucity of literature that has delved into...The advancement of Artificial Intelligence(AI)has garnered significant attention within the academic research community,reflecting the prevailing zeitgeist.However,there is a paucity of literature that has delved into its connection with the higher order thinking skills of human beings.The purpose of this systematic review is to investigate the relationship between AI utilization and the development of critical thinking(CT)in the field of education.A systematic literature search was performed in two databases,Web of Science and Scopus,with a focus on empirical studies related to AI and CT.The review process followed the PRISMA framework and adopted a bottom-up approach,Ultimately,the integrated review synthesized 21 eligible studies from a total of 649 articles.The systematic review identified three relationships between AI technologies and CT.The results suggest that AI technologies can help to enhance learners’CT skills(n=13).However,excessive or inappropriate utilization of AI may hinder CT development(n=7).Moreover,the cultivation of CT skills has been shown to positively influence the effectiveness of AI utilization(n=4).This article represents the first literature review to delve into the reciprocal relationship between AI implementation and CT development within the education field,striving to illuminate the ways in which learners can enhance their higher-order thinking skills through more effective utilization of AI technologies.展开更多
Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds.Accurate data analysis is crucial for converting external stimuli from each artificial sense in...Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds.Accurate data analysis is crucial for converting external stimuli from each artificial sense into user-relevant information,yet conventional signal processing methods struggle with the massive scale,noise,and artificial sensory systems characteristics of data generated by artificial sensory devices.Integrating artificial intelligence(AI)is essential for addressing these challenges and enhancing the performance of artificial sensory systems,making it a rapidly growing area of research in recent years.However,no studies have systematically categorized the output functions of these systems or analyzed the associated AI algorithms and data processing methods.In this review,we present a systematic overview of the latest AI techniques aimed at enhancing the cognitive capabilities of artificial sensory systems replicating the five human senses:touch,taste,vision,smell,and hearing.We categorize the AI-enabled capabilities of artificial sensory systems into four key areas:cognitive simulation,perceptual enhancement,adaptive adjustment,and early warning.We introduce specialized AI algorithms and raw data processing methods for each function,designed to enhance and optimize sensing performance.Finally,we offer a perspective on the future of AI-integrated artificial sensory systems,highlighting technical challenges and potential real-world application scenarios for further innovation.Integration of AI with artificial sensory systems will enable advanced multimodal perception,real-time learning,and predictive capabilities.This will drive precise environmental adaptation and personalized feedback,ultimately positioning these systems as foundational technologies in smart healthcare,agriculture,and automation.展开更多
Compressed air energy storage(CAES)has emerged as a grid-scale energy storage linchpin,providing diurnal-to-seasonal timescale energy buffering for renewable power integration.Diverging from conventional salt cavernde...Compressed air energy storage(CAES)has emerged as a grid-scale energy storage linchpin,providing diurnal-to-seasonal timescale energy buffering for renewable power integration.Diverging from conventional salt caverndependent approaches,artificial cavern-based CAES unlocks geographical adaptability through engineered underground containment.This study systematically reviews critical technologies in chamber construction,including site selection,structural design,excavation methods,and post-construction evaluation.Site selection employs a multi-criteria matrix that combines geological and environmental factors.Structural design integrates spatial layout,burial depth,sealing system,and component compatibility to ensure chamber stability.Excavation prioritizes controlled blasting for homogeneous rock,while a tunnel boring machine is deployed in fractured zones to preserve integrity.Postconstruction assessments validate load-bearing capacity,sealing performance,and operational readiness,supported by data-driven maintenance strategies.Ongoing challenges include site-specific geological risks,sealing system durability under cyclic loading,equipment integration,field-scale validation,standardization gaps,and cost-efficiency optimization.These innovations will establish best practices for building large-scale,high-efficiency CAES plants with ultra-long duration and grid resilience,accelerating the transition to carbon-neutral power systems.展开更多
Public-and private-sector organizations have adopted artificial intelligence(AI)to meet the challenges of the Fourth Industrial Revolution.The successful implementation of AI is a challenging task,and previous researc...Public-and private-sector organizations have adopted artificial intelligence(AI)to meet the challenges of the Fourth Industrial Revolution.The successful implementation of AI is a challenging task,and previous research has advocated the need to explore key readiness before AI implementation.The objective of this study is to identify the AI readiness factors explored by different authors in past research.To achieve this,we conducted a rigorous literature review.The approach used in the systematic literature review is also discussed.A rigorous review of 52 studies from various journals and databases(Science Direct,Springer Link,Institute of Electrical and Electronics Engineers,Emerald,and Google Scholar)identified 23 AI readiness factors.The key factors identified were mainly related to organizational information technology infrastructure,top management support,resource availability,collaborative culture,organizational size,organizational capability,compatibility,data quality,and financial budget,whereas the other 15 were potential factors in AI readiness.All of these factors should be considered before the implementation of AI in any organization.The findings also reflect a high failure rate,including AI readiness factors,which are intended to facilitate AI adoption in organizations and reduce the frequency of failures.These factors will aid management in developing an effective strategy for AI implementation in organizations.展开更多
Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now en...Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.展开更多
Van der Waals(vdW)ferroelectric-semiconductor heterojunction provides reconfigurable band alignment based on optical/electrical-assisted polarization switching,which shows great potential to construct artificial visua...Van der Waals(vdW)ferroelectric-semiconductor heterojunction provides reconfigurable band alignment based on optical/electrical-assisted polarization switching,which shows great potential to construct artificial visual neural systems.However,the mechanical exfoliation fabrication scheme for proof-of-concept demonstrations and fundamental studies is cumbersome and not scalable for practical application.Here,we present a synthetic strategy for the large-scale and high crystallinity growth of planar/verticalα-In_(2)Se_(3)/MoS_(2)heterojunctions by dynamically tuning the growth temperature.Furthermore,based on theα-In_(2)Se_(3)/MoS_(2)heterostructures,photo-synapse devices are designed and fabricated to simulate visual neural systems functions,including multistate storage,optical logic operation,potentiation and depression,paired-pulse facilitation(PPF),short-term memory(STM),long-term memory(LTM),and Learning-Forgetting-Relearning.By coupling the spatiotemporally relevant optical and electric information,the device can mimic the superior biological visual system’s light adaptation and Pavlovian conditioning.This work provides a strategy for dynamically tuning the orientation of ferroelectric-semiconductor heterojunction stacks and will give impetus to applying all-in-one sensing and memory-computing artificial vision systems.展开更多
Objective:To explore the application value of artificial intelligence-assisted diagnostic systems in the computed tomography(CT)diagnosis of pulmonary nodules.Methods:A total of 80 patients with pulmonary nodules,trea...Objective:To explore the application value of artificial intelligence-assisted diagnostic systems in the computed tomography(CT)diagnosis of pulmonary nodules.Methods:A total of 80 patients with pulmonary nodules,treated from June 2023 to May 2024,were included.All patients underwent pathological examination and CT scans,with pathological results serving as the gold standard.The diagnostic performance of CT alone and CT combined with the artificial intelligence-assisted diagnostic system was analyzed,and differences in CT imaging features and evaluation results of benign and malignant pulmonary nodules were compared.Results:The sensitivity,specificity,and accuracy of CT combined with the artificial intelligence-assisted diagnostic system were significantly higher than those of CT alone(P<0.05).Moreover,the false-positive and false-negative rates were significantly lower for the combined approach compared to CT alone(P<0.05).Conclusion:The artificial intelligence-assisted diagnostic system effectively identifies malignant features in pulmonary nodules,providing valuable clinical reference data and enhancing diagnostic accuracy and efficiency.展开更多
BACKGROUND Artificial intelligence(AI)is transforming healthcare by improving diagnostic accuracy and predictive analytics.Periodontal diseases are recognized as risk factors for systemic conditions,including type 2 d...BACKGROUND Artificial intelligence(AI)is transforming healthcare by improving diagnostic accuracy and predictive analytics.Periodontal diseases are recognized as risk factors for systemic conditions,including type 2 diabetes mellitus,cardiovascular disease,Alzheimer’s disease,polycystic ovary syndrome,thyroid dysfunction,and post-coronavirus disease 2019 complications.These conditions exhibit complex bidirectional interactions,underscoring the importance of early detection and risk stratification.Current diagnostic tools often fail to capture these interactions at an early stage,limiting timely intervention.This study hypothesizes that AI-driven approaches can significantly improve early diagnosis and risk prediction of periodontal-systemic interactions,enhancing clinical outcomes.AIM To evaluate AI’s role in diagnosing and predicting periodontal-systemic interactions in studies from 2010 to 2024.METHODS This systematic review followed PRISMA guidelines(2009)and included peerreviewed articles from PubMed,Scopus,and Embase.Studies with large sample sizes(≥500 participants)were selected,focusing on AI models integrating multiomics data and advanced imaging techniques such as cone beam computed tomography and magnetic resonance imaging.Machine learning models processed structured clinical data,deep learning models combined imaging and clinical data,and natural language processing models extracted insights from clinical notes.RESULTS AI applications significantly enhanced diagnostic and predictive accuracy,reducing diagnostic time by 40%and improving predictive accuracy by 25%in periodontal patients with type 2 diabetes mellitus.Studies with sample sizes of 1000-1500 participants reported diagnostic accuracy improvements up to 92%,with specificity and sensitivity rates of 94%and 90%,respectively.Increasing sample sizes over the years reflected advancements in AI,data collection,and model training,reinforcing model reliability.CONCLUSION AI’s integration of multi-omics and imaging data has transformed early diagnosis and risk prediction in periodontal-systemic interactions,improving clinical outcomes and decision-making.展开更多
BACKGROUND Recent advancements in artificial intelligence(AI)have significantly enhanced the capabilities of endoscopic-assisted diagnosis for gastrointestinal diseases.AI has shown great promise in clinical practice,...BACKGROUND Recent advancements in artificial intelligence(AI)have significantly enhanced the capabilities of endoscopic-assisted diagnosis for gastrointestinal diseases.AI has shown great promise in clinical practice,particularly for diagnostic support,offering real-time insights into complex conditions such as esophageal squamous cell carcinoma.CASE SUMMARY In this study,we introduce a multimodal AI system that successfully identified and delineated a small and flat carcinoma during esophagogastroduodenoscopy,highlighting its potential for early detection of malignancies.The lesion was confirmed as high-grade squamous intraepithelial neoplasia,with pathology results supporting the AI system’s accuracy.The multimodal AI system offers an integrated solution that provides real-time,accurate diagnostic information directly within the endoscopic device interface,allowing for single-monitor use without disrupting endoscopist’s workflow.CONCLUSION This work underscores the transformative potential of AI to enhance endoscopic diagnosis by enabling earlier,more accurate interventions.展开更多
BACKGROUND Colorectal cancer(CRC)can be prevented by screening and early detection.Colonoscopy is used for screening,and adenoma detection rate(ADR)is used as a key quality indicator of sufficient colonoscopy.However,...BACKGROUND Colorectal cancer(CRC)can be prevented by screening and early detection.Colonoscopy is used for screening,and adenoma detection rate(ADR)is used as a key quality indicator of sufficient colonoscopy.However,ADR can vary significantly among endoscopists,leading to missed polyps or cancer.Artificial intelligence(AI)has shown promise in improving ADR by assisting in real-time polyp identification or diagnosis.While multiple randomized controlled trials(RCTs)and metanalyses highlight the benefits of AI in increasing detection rates and reducing missed polyps,concerns remain about its real-world applicability,impact on procedure time,and cost-effectiveness.AIM To explore the current status of AI assistance colonoscopy in adenoma detection and improving quality of colonoscopy.METHODS This systematic review followed PRISMA guidelines,both PubMed and Web of Science databases were used for articles search.Metanalyses and systematic reviews that assessed AI's role during colonoscopy.English article only published between January 2000 and January 2025 were included.Articles related to nonadenoma indications were excluded.Data extraction was independently performed by two researchers for accuracy and consistency.RESULTS 22 articles met the inclusion criteria,with significant heterogeneity(I2=28%-91%)observed in multiple studies.The number of studies per metanalysis ranged from 5 to 33,with higher heterogeneity in analyses involving more than 18 RCTs.AI demonstrated improvement in ADR,with an approximate 20%increase across multiple studies.However,its effectiveness in detecting flat or serrated adenomas remains unproven.Endoscopists with low ADR benefit more from AI-colonoscopies,while expert endoscopists outperformed AI in ADR,adenoma miss rate,and the identification of advanced lesions.No significant change in withdrawal time was observed when comparing AI-assisted colonoscopy to conventional endoscopy.CONCLUSION While AI-assisted colonoscopy has been shown to improve procedural quality,particularly for junior endoscopists and those with lower ADR,its performance decreases when compared to expert endoscopists in real-time clinical practice.This is especially evident in non-randomized studies,where AI demonstrates limited real-world benefits despite its benefit in controlled settings.Furthermore,no meta-analyses have specifically examined AI's impact on the learning experience of fellows and residents.Some experts caution that reliance on AI may prevent trainees from developing essential observational skills,potentially leading to less thorough examinations.Further research is needed to determine the actual benefits of AI-colonoscopy,particularly its role in cancer prevention.As technology advances,improved outcomes are expected,especially in detecting small,flat,and lesions at difficult anatomical locations.展开更多
This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant att...This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.展开更多
This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technol...This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technology deployment relevant to disease management,healthcare delivery,epidemiology,and policy-making.However,its AI is culturally sensitive and ethically grounded in Islam.Based on the PRISMA framework,an SLR evaluated primary academic literature,cases,and practices of Saudi Arabia’s AI implementation in the public healthcare sector.Instead,it categorizes prior research based on how AI can work,the issues it poses,and its implications for the Kingdom’s healthcare system.The Saudi Arabian context analyses show that AI has increased the discreet prediction of diseases,resource management,and monitoring outbreaks during mass congregations such as hajj.Therefore,the study outlines critical areas for defining the potential for artificial intelligence and areas for enhancing digital development to support global healthcare progress.The key themes emerging from the review include Saudi Arabia:(i)the effectiveness of AI with human interaction for sustainable health services;(ii)conditions and quality control to enhance the quality of health care services using AI;(iii)environmental factors as influencing factors for public health care;(iv)Artificial Intelligence,and advanced decision-making technology for Middle Eastern health care systems.For policymakers,healthcare managers,and researchers who will engage with AI innovation,the review proclaims that AI applications should respect the country’s socio-cultural and ethical practices and pave the way for sustainable healthcare provision.More empirical research is needed on the implementation issues with AI,creating culturally appropriate models of AI,and finding new applications of AI to address the increasing demand for healthcare services in Saudi Arabia.展开更多
Artificial sensory systems,designed to emulate human senses like sight,touch,and hearing,have garnered significant attention for their potential to enhance human capabilities,improve human-machine interactions,and ena...Artificial sensory systems,designed to emulate human senses like sight,touch,and hearing,have garnered significant attention for their potential to enhance human capabilities,improve human-machine interactions,and enable autonomous systems to better perceive their surroundings.Hydrogels,with their biocompatibility,flexibility,and water-rich polymer structure,are increasingly recognized as crucial materials in the development of these systems,especially in applications such as wearable sensors,artificial skin,and neural interfaces.This review explores various hydrogel fabrication techniques,including 3D bioprinting,electro spinning,and photopolymerization,which allow for the precise control of hydrogel properties like mechanical strength,flexibility,and conductivity.By tailoring these properties to mimic natural tissues,hydrogels offer transformative benefits in the creation of advanced,biocompatible,and durable sensory systems.We emphasize the importance of selecting appropriate fabrication methods to meet the specific functional requirements of artificial sensory applications,such as sensitivity to stimuli,durability,and ease of integration.This review further highlights the pivotal role of hydrogels in advancing future artificial sensory technologies and their broad potential in fields ranging from robotics to biomedical devices.展开更多
基金the NEDO＇s International Joint Research Grant Program and the National Science Foundation of China （No. 20573025） for the financial supports.
文摘Hydrogen evolution was detected in an artificial system composed of light-harvesting unit of purified photosystem I, catalyst of hydrogenase, methyl viologen and electron donor under radiation. Absorption spectral features confirmed that electron transfer from electron donors to proton was via a photoinduced reductive process of methyl viologen.
文摘Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2023-00242528,50%)supported by the Korea Internet&Security Agency(KISA)through the Information Security Specialized University Support Project(50%).
文摘As attack techniques evolve and data volumes increase,the integration of artificial intelligence-based security solutions into industrial control systems has become increasingly essential.Artificial intelligence holds significant potential to improve the operational efficiency and cybersecurity of these systems.However,its dependence on cyber-based infrastructures expands the attack surface and introduces the risk that adversarial manipulations of artificial intelligence models may cause physical harm.To address these concerns,this study presents a comprehensive review of artificial intelligence-driven threat detection methods and adversarial attacks targeting artificial intelligence within industrial control environments,examining both their benefits and associated risks.A systematic literature review was conducted across major scientific databases,including IEEE,Elsevier,Springer Nature,ACM,MDPI,and Wiley,covering peer-reviewed journal and conference papers published between 2017 and 2026.Studies were selected based on predefined inclusion and exclusion criteria following a structured screening process.Based on an analysis of 101 selected studies,this survey categorizes artificial intelligence-based threat detection approaches across the physical,control,and application layers of industrial control systems and examines poisoning,evasion,and extraction attacks targeting industrial artificial intelligence.The findings identify key research trends,highlight unresolved security challenges,and discuss implications for the secure deployment of artificial intelligence-enabled cybersecurity solutions in industrial control systems.
文摘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.
文摘Background:Artificial intelligence(AI)-assisted threedimensional(3D)surgical platforms,integrated with augmented reality,have the potential to improve intraoperative anatomical recognition and provide surgeons with an immersive,dynamic operating environment during urooncological procedures.This review aims to examine the current applications of AI in robotic uro-oncology,with a particular focus on its role in facilitating intraoperative navigation during complex surgeries.Methods:A systematic literature search was performed across PubMed,the National Library of Medicine,MEDLINE,the Cochrane Central Register of Controlled Trials(CENTRAL),ClinicalTrials.gov,and Google Scholar to identify relevant studies published up to July 2025.The search strategy incorporated a predefined set of keywords,including AI,machine learning,radical prostatectomy(RP),robotic-assisted radical prostatectomy(RARP),robotassisted partial nephrectomy(RAPN),and robot-assisted radical cystectomy(RARC).Only clinical trials,full-text peer-reviewed publications,and original research articles were included.Studies were eligible for inclusion if they evaluated or described applications of AI in RARP,RAPN,or RARC.Results:Technological advancements have substantially transformed the field of uro-oncologic surgery.In particular,AI and AI-assisted intraoperative navigation in RARP demonstrate considerable potential to objectively assess surgical performance and predict clinical outcomes.In RAPN,the adoption of preoperative,interactive 3D virtualmodels for surgical planning has influenced surgical decisions,thus,enhanced precision in resection planning correlates with superior nephron-sparing outcomes and optimized selective clamping.AI applications in RARC,techniques such as augmented reality(AR)can overlay critical information on the surgical field,by facilitating navigation through complex anatomical planes and enhancing identification of critical structures.Conclusion:AI appears to enhance robotic uro-oncologic procedures by increasing operative precision and supporting individualised surgical treatment strategies.
基金Supported by Beijing Municipal Natural Science Foundation of China(Grant No.24JL002)China Postdoctoral Science Foundation(Grant No.2024M754054)+2 种基金National Natural Science Foundation of China(Grant No.52120105008)Beijing Municipal Outstanding Young Scientis Program of Chinathe New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.
基金funded by Macao Polytechnic University grant number RP/FCA-08-2023.
文摘The advancement of Artificial Intelligence(AI)has garnered significant attention within the academic research community,reflecting the prevailing zeitgeist.However,there is a paucity of literature that has delved into its connection with the higher order thinking skills of human beings.The purpose of this systematic review is to investigate the relationship between AI utilization and the development of critical thinking(CT)in the field of education.A systematic literature search was performed in two databases,Web of Science and Scopus,with a focus on empirical studies related to AI and CT.The review process followed the PRISMA framework and adopted a bottom-up approach,Ultimately,the integrated review synthesized 21 eligible studies from a total of 649 articles.The systematic review identified three relationships between AI technologies and CT.The results suggest that AI technologies can help to enhance learners’CT skills(n=13).However,excessive or inappropriate utilization of AI may hinder CT development(n=7).Moreover,the cultivation of CT skills has been shown to positively influence the effectiveness of AI utilization(n=4).This article represents the first literature review to delve into the reciprocal relationship between AI implementation and CT development within the education field,striving to illuminate the ways in which learners can enhance their higher-order thinking skills through more effective utilization of AI technologies.
基金supported by the National Research Foundation(NRF)grant funded by the Korean government(MSIT)(RS-2023-00211580,RS-2023-00237308).
文摘Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds.Accurate data analysis is crucial for converting external stimuli from each artificial sense into user-relevant information,yet conventional signal processing methods struggle with the massive scale,noise,and artificial sensory systems characteristics of data generated by artificial sensory devices.Integrating artificial intelligence(AI)is essential for addressing these challenges and enhancing the performance of artificial sensory systems,making it a rapidly growing area of research in recent years.However,no studies have systematically categorized the output functions of these systems or analyzed the associated AI algorithms and data processing methods.In this review,we present a systematic overview of the latest AI techniques aimed at enhancing the cognitive capabilities of artificial sensory systems replicating the five human senses:touch,taste,vision,smell,and hearing.We categorize the AI-enabled capabilities of artificial sensory systems into four key areas:cognitive simulation,perceptual enhancement,adaptive adjustment,and early warning.We introduce specialized AI algorithms and raw data processing methods for each function,designed to enhance and optimize sensing performance.Finally,we offer a perspective on the future of AI-integrated artificial sensory systems,highlighting technical challenges and potential real-world application scenarios for further innovation.Integration of AI with artificial sensory systems will enable advanced multimodal perception,real-time learning,and predictive capabilities.This will drive precise environmental adaptation and personalized feedback,ultimately positioning these systems as foundational technologies in smart healthcare,agriculture,and automation.
基金National Natural Science Foundation of China,Grant/Award Number:52474080National Key R&D Program of China,Grant/Award Number:2024YFB4007100。
文摘Compressed air energy storage(CAES)has emerged as a grid-scale energy storage linchpin,providing diurnal-to-seasonal timescale energy buffering for renewable power integration.Diverging from conventional salt caverndependent approaches,artificial cavern-based CAES unlocks geographical adaptability through engineered underground containment.This study systematically reviews critical technologies in chamber construction,including site selection,structural design,excavation methods,and post-construction evaluation.Site selection employs a multi-criteria matrix that combines geological and environmental factors.Structural design integrates spatial layout,burial depth,sealing system,and component compatibility to ensure chamber stability.Excavation prioritizes controlled blasting for homogeneous rock,while a tunnel boring machine is deployed in fractured zones to preserve integrity.Postconstruction assessments validate load-bearing capacity,sealing performance,and operational readiness,supported by data-driven maintenance strategies.Ongoing challenges include site-specific geological risks,sealing system durability under cyclic loading,equipment integration,field-scale validation,standardization gaps,and cost-efficiency optimization.These innovations will establish best practices for building large-scale,high-efficiency CAES plants with ultra-long duration and grid resilience,accelerating the transition to carbon-neutral power systems.
文摘Public-and private-sector organizations have adopted artificial intelligence(AI)to meet the challenges of the Fourth Industrial Revolution.The successful implementation of AI is a challenging task,and previous research has advocated the need to explore key readiness before AI implementation.The objective of this study is to identify the AI readiness factors explored by different authors in past research.To achieve this,we conducted a rigorous literature review.The approach used in the systematic literature review is also discussed.A rigorous review of 52 studies from various journals and databases(Science Direct,Springer Link,Institute of Electrical and Electronics Engineers,Emerald,and Google Scholar)identified 23 AI readiness factors.The key factors identified were mainly related to organizational information technology infrastructure,top management support,resource availability,collaborative culture,organizational size,organizational capability,compatibility,data quality,and financial budget,whereas the other 15 were potential factors in AI readiness.All of these factors should be considered before the implementation of AI in any organization.The findings also reflect a high failure rate,including AI readiness factors,which are intended to facilitate AI adoption in organizations and reduce the frequency of failures.These factors will aid management in developing an effective strategy for AI implementation in organizations.
基金supported by the National Key Research and Development Program (No.2023YFC3502604)the National Natural Science Foundation of China (Nos.U23B2062, 82274352,82174533, 82374302, 82204941)+3 种基金the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No.2023ZD0505700)the Beijing-Tianjin-Hebei Basic Research Cooperation Project (No.22JCZXJC00070)the State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture (No.SKL2024Z0102)Key R&D project of Ningxia Autonomous Region (No.2022BEG02036).
文摘Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.
基金supported by the National Natural Science Foundation of China(Nos.52371245,12174237,12241403)the National Key Research and Development Program of China(No.2022YFB3505301).
文摘Van der Waals(vdW)ferroelectric-semiconductor heterojunction provides reconfigurable band alignment based on optical/electrical-assisted polarization switching,which shows great potential to construct artificial visual neural systems.However,the mechanical exfoliation fabrication scheme for proof-of-concept demonstrations and fundamental studies is cumbersome and not scalable for practical application.Here,we present a synthetic strategy for the large-scale and high crystallinity growth of planar/verticalα-In_(2)Se_(3)/MoS_(2)heterojunctions by dynamically tuning the growth temperature.Furthermore,based on theα-In_(2)Se_(3)/MoS_(2)heterostructures,photo-synapse devices are designed and fabricated to simulate visual neural systems functions,including multistate storage,optical logic operation,potentiation and depression,paired-pulse facilitation(PPF),short-term memory(STM),long-term memory(LTM),and Learning-Forgetting-Relearning.By coupling the spatiotemporally relevant optical and electric information,the device can mimic the superior biological visual system’s light adaptation and Pavlovian conditioning.This work provides a strategy for dynamically tuning the orientation of ferroelectric-semiconductor heterojunction stacks and will give impetus to applying all-in-one sensing and memory-computing artificial vision systems.
基金supported by Chengdu University of Traditional Chinese Medicine“Xinglin Scholars”Subject Talent Scientific Research Enhancement Plan(No.YYZX2022056).
文摘Objective:To explore the application value of artificial intelligence-assisted diagnostic systems in the computed tomography(CT)diagnosis of pulmonary nodules.Methods:A total of 80 patients with pulmonary nodules,treated from June 2023 to May 2024,were included.All patients underwent pathological examination and CT scans,with pathological results serving as the gold standard.The diagnostic performance of CT alone and CT combined with the artificial intelligence-assisted diagnostic system was analyzed,and differences in CT imaging features and evaluation results of benign and malignant pulmonary nodules were compared.Results:The sensitivity,specificity,and accuracy of CT combined with the artificial intelligence-assisted diagnostic system were significantly higher than those of CT alone(P<0.05).Moreover,the false-positive and false-negative rates were significantly lower for the combined approach compared to CT alone(P<0.05).Conclusion:The artificial intelligence-assisted diagnostic system effectively identifies malignant features in pulmonary nodules,providing valuable clinical reference data and enhancing diagnostic accuracy and efficiency.
文摘BACKGROUND Artificial intelligence(AI)is transforming healthcare by improving diagnostic accuracy and predictive analytics.Periodontal diseases are recognized as risk factors for systemic conditions,including type 2 diabetes mellitus,cardiovascular disease,Alzheimer’s disease,polycystic ovary syndrome,thyroid dysfunction,and post-coronavirus disease 2019 complications.These conditions exhibit complex bidirectional interactions,underscoring the importance of early detection and risk stratification.Current diagnostic tools often fail to capture these interactions at an early stage,limiting timely intervention.This study hypothesizes that AI-driven approaches can significantly improve early diagnosis and risk prediction of periodontal-systemic interactions,enhancing clinical outcomes.AIM To evaluate AI’s role in diagnosing and predicting periodontal-systemic interactions in studies from 2010 to 2024.METHODS This systematic review followed PRISMA guidelines(2009)and included peerreviewed articles from PubMed,Scopus,and Embase.Studies with large sample sizes(≥500 participants)were selected,focusing on AI models integrating multiomics data and advanced imaging techniques such as cone beam computed tomography and magnetic resonance imaging.Machine learning models processed structured clinical data,deep learning models combined imaging and clinical data,and natural language processing models extracted insights from clinical notes.RESULTS AI applications significantly enhanced diagnostic and predictive accuracy,reducing diagnostic time by 40%and improving predictive accuracy by 25%in periodontal patients with type 2 diabetes mellitus.Studies with sample sizes of 1000-1500 participants reported diagnostic accuracy improvements up to 92%,with specificity and sensitivity rates of 94%and 90%,respectively.Increasing sample sizes over the years reflected advancements in AI,data collection,and model training,reinforcing model reliability.CONCLUSION AI’s integration of multi-omics and imaging data has transformed early diagnosis and risk prediction in periodontal-systemic interactions,improving clinical outcomes and decision-making.
基金Supported by the 135 High-end Talent Project of West China Hospital,Sichuan University,No.ZYDG23029.
文摘BACKGROUND Recent advancements in artificial intelligence(AI)have significantly enhanced the capabilities of endoscopic-assisted diagnosis for gastrointestinal diseases.AI has shown great promise in clinical practice,particularly for diagnostic support,offering real-time insights into complex conditions such as esophageal squamous cell carcinoma.CASE SUMMARY In this study,we introduce a multimodal AI system that successfully identified and delineated a small and flat carcinoma during esophagogastroduodenoscopy,highlighting its potential for early detection of malignancies.The lesion was confirmed as high-grade squamous intraepithelial neoplasia,with pathology results supporting the AI system’s accuracy.The multimodal AI system offers an integrated solution that provides real-time,accurate diagnostic information directly within the endoscopic device interface,allowing for single-monitor use without disrupting endoscopist’s workflow.CONCLUSION This work underscores the transformative potential of AI to enhance endoscopic diagnosis by enabling earlier,more accurate interventions.
文摘BACKGROUND Colorectal cancer(CRC)can be prevented by screening and early detection.Colonoscopy is used for screening,and adenoma detection rate(ADR)is used as a key quality indicator of sufficient colonoscopy.However,ADR can vary significantly among endoscopists,leading to missed polyps or cancer.Artificial intelligence(AI)has shown promise in improving ADR by assisting in real-time polyp identification or diagnosis.While multiple randomized controlled trials(RCTs)and metanalyses highlight the benefits of AI in increasing detection rates and reducing missed polyps,concerns remain about its real-world applicability,impact on procedure time,and cost-effectiveness.AIM To explore the current status of AI assistance colonoscopy in adenoma detection and improving quality of colonoscopy.METHODS This systematic review followed PRISMA guidelines,both PubMed and Web of Science databases were used for articles search.Metanalyses and systematic reviews that assessed AI's role during colonoscopy.English article only published between January 2000 and January 2025 were included.Articles related to nonadenoma indications were excluded.Data extraction was independently performed by two researchers for accuracy and consistency.RESULTS 22 articles met the inclusion criteria,with significant heterogeneity(I2=28%-91%)observed in multiple studies.The number of studies per metanalysis ranged from 5 to 33,with higher heterogeneity in analyses involving more than 18 RCTs.AI demonstrated improvement in ADR,with an approximate 20%increase across multiple studies.However,its effectiveness in detecting flat or serrated adenomas remains unproven.Endoscopists with low ADR benefit more from AI-colonoscopies,while expert endoscopists outperformed AI in ADR,adenoma miss rate,and the identification of advanced lesions.No significant change in withdrawal time was observed when comparing AI-assisted colonoscopy to conventional endoscopy.CONCLUSION While AI-assisted colonoscopy has been shown to improve procedural quality,particularly for junior endoscopists and those with lower ADR,its performance decreases when compared to expert endoscopists in real-time clinical practice.This is especially evident in non-randomized studies,where AI demonstrates limited real-world benefits despite its benefit in controlled settings.Furthermore,no meta-analyses have specifically examined AI's impact on the learning experience of fellows and residents.Some experts caution that reliance on AI may prevent trainees from developing essential observational skills,potentially leading to less thorough examinations.Further research is needed to determine the actual benefits of AI-colonoscopy,particularly its role in cancer prevention.As technology advances,improved outcomes are expected,especially in detecting small,flat,and lesions at difficult anatomical locations.
基金financial support from the Fundamental Research Grant Scheme(FRGS)under grant number:FRGS/1/2024/ICT02/TARUMT/02/1from the Ministry of Higher Education Malaysiafunded in part by the internal grant from the Tunku Abdul Rahman University of Management and Technology(TAR UMT)with grant number:UC/I/G2024-00129.
文摘This study systematically reviews the applications of generative artificial intelligence(GAI)in breast cancer research,focusing on its role in diagnosis and therapeutic development.While GAI has gained significant attention across various domains,its utility in breast cancer research has yet to be comprehensively reviewed.This study aims to fill that gap by synthesizing existing research into a unified document.A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,resulting in the retrieval of 3827 articles,of which 31 were deemed eligible for analysis.The included studies were categorized based on key criteria,such as application types,geographical distribution,contributing organizations,leading journals,publishers,and temporal trends.Keyword co-occurrence mapping and subject profiling further highlighted the major research themes in this field.The findings reveal that GAI models have been applied to improve breast cancer diagnosis,treatment planning,and outcome predictions.Geographical and network analyses showed that most contributions come from a few leading institutions,with limited global collaboration.The review also identifies key challenges in implementing GAI in clinical practice,such as data availability,ethical concerns,and model validation.Despite these challenges,the study highlights GAI’s potential to enhance breast cancer research,particularly in generating synthetic data,improving diagnostic accuracy,and personalizing treatment approaches.This review serves as a valuable resource for researchers and stakeholders,providing insights into current research trends,major contributors,and collaborative networks in GAI-based breast cancer studies.By offering a holistic overview,it aims to support future research directions and encourage broader adoption of GAI technologies in healthcare.Additionally,the study emphasizes the importance of overcoming implementation barriers to fully realizeGAI’s potential in transforming breast cancer management.
基金funded by the Scientific ResearchDeanship at theUniversity ofHa’il-Saudi Arabia through project number-RG-23251.
文摘This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technology deployment relevant to disease management,healthcare delivery,epidemiology,and policy-making.However,its AI is culturally sensitive and ethically grounded in Islam.Based on the PRISMA framework,an SLR evaluated primary academic literature,cases,and practices of Saudi Arabia’s AI implementation in the public healthcare sector.Instead,it categorizes prior research based on how AI can work,the issues it poses,and its implications for the Kingdom’s healthcare system.The Saudi Arabian context analyses show that AI has increased the discreet prediction of diseases,resource management,and monitoring outbreaks during mass congregations such as hajj.Therefore,the study outlines critical areas for defining the potential for artificial intelligence and areas for enhancing digital development to support global healthcare progress.The key themes emerging from the review include Saudi Arabia:(i)the effectiveness of AI with human interaction for sustainable health services;(ii)conditions and quality control to enhance the quality of health care services using AI;(iii)environmental factors as influencing factors for public health care;(iv)Artificial Intelligence,and advanced decision-making technology for Middle Eastern health care systems.For policymakers,healthcare managers,and researchers who will engage with AI innovation,the review proclaims that AI applications should respect the country’s socio-cultural and ethical practices and pave the way for sustainable healthcare provision.More empirical research is needed on the implementation issues with AI,creating culturally appropriate models of AI,and finding new applications of AI to address the increasing demand for healthcare services in Saudi Arabia.
基金supported by the National Research Foundation of Korea(NRF)Grants funded by the Korea government(MSIT)(Nos.RS-2023-00213047 and RS-2024-00405818)。
文摘Artificial sensory systems,designed to emulate human senses like sight,touch,and hearing,have garnered significant attention for their potential to enhance human capabilities,improve human-machine interactions,and enable autonomous systems to better perceive their surroundings.Hydrogels,with their biocompatibility,flexibility,and water-rich polymer structure,are increasingly recognized as crucial materials in the development of these systems,especially in applications such as wearable sensors,artificial skin,and neural interfaces.This review explores various hydrogel fabrication techniques,including 3D bioprinting,electro spinning,and photopolymerization,which allow for the precise control of hydrogel properties like mechanical strength,flexibility,and conductivity.By tailoring these properties to mimic natural tissues,hydrogels offer transformative benefits in the creation of advanced,biocompatible,and durable sensory systems.We emphasize the importance of selecting appropriate fabrication methods to meet the specific functional requirements of artificial sensory applications,such as sensitivity to stimuli,durability,and ease of integration.This review further highlights the pivotal role of hydrogels in advancing future artificial sensory technologies and their broad potential in fields ranging from robotics to biomedical devices.
