The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary.This paper provides the outcomes of development and complex testing of the artifi...The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary.This paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker’s functionality.In this work,we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming environment.The electrical model of the heart allows signals generation(right atrium,right ventricle)and the monitoring of the stimulation pulses.The LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in real-time.The results demonstrate the capability of proposed system to evaluate the paced and sensed pulses.The proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing mode.In addition,the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.展开更多
Objective: To analyze the effect of continuous nursing on self-care ability and quality of life of patients with permanent artificial pacemaker implantation. Methods: A total of 90 patients receiving permanent artific...Objective: To analyze the effect of continuous nursing on self-care ability and quality of life of patients with permanent artificial pacemaker implantation. Methods: A total of 90 patients receiving permanent artificial pacemaker treatment in our hospital during the first 8 months of November 2021 were selected as samples to compare the data differences between the two groups (control group and intervention group were 45 patients /n = 45 patients/group, respectively). Results: The compliance of the intervention group was higher than that of the control group (P;There was no significant difference in EDCA score between groups before intervention (P>0.05). After intervention, EDCA score in the intervention group was higher than that in the control group (P<0.05). The nursing quality score of intervention group was higher than that of control group (P;The compliance score of intervention group was higher than that of control group (P<0.05). After intervention, SRSS and MNA scores in the intervention group were higher than those in the control group (P<0.05). The scores of the four items in the intervention group were higher than those in the control group (P < 0.05). Discussion: Continuous nursing intervention can effectively improve the quality and effect of nursing, and has significant application value.展开更多
Artificial intelligence(AI)is a new arena for human technological development,and one of the most concerning global governance issues at present.In recent years,breakthroughs in generative AI technologies have been ma...Artificial intelligence(AI)is a new arena for human technological development,and one of the most concerning global governance issues at present.In recent years,breakthroughs in generative AI technologies have been made,and the prospects of large-scale application of AI technologies have become ever brighter,bringing us closer to the artificial general intelligence(AGI)that can enable machines to think and act like humans.As a strategic technology leading a new round of technological revolution and industrial transformation,AI offers enormous opportunities to advance human society,yet it also introduces significant security risks and challenges.How to maximize the development potential of AI at the global level while establishing an effective international governance framework has become a focus of global concern.展开更多
With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing archit...With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution.Neuromorphic computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI technology.Artificial neurons and synapses are the two core components of neuromorphic computing systems.Artificial perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research.This work reviews recent advances in artificial sensory neurons and their applications.First,biological sensory neurons are briefly described.Then,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working mechanisms.Next,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and smell.Finally,challenges faced by artificial sensory neurons at both device and system levels are summarized.展开更多
Dealing with data scarcity is the biggest challenge faced by Artificial Intelligence(AI),and it will be interesting to see how we overcome this obstacle in the future,but for now,“THE SHOW MUST GO ON!!!”As AI spread...Dealing with data scarcity is the biggest challenge faced by Artificial Intelligence(AI),and it will be interesting to see how we overcome this obstacle in the future,but for now,“THE SHOW MUST GO ON!!!”As AI spreads and transforms more industries,the lack of data is a significant obstacle:the best methods for teaching machines how real-world processes work.This paper explores the considerable implications of data scarcity for the AI industry,which threatens to restrict its growth and potential,and proposes plausible solutions and perspectives.In addition,this article focuses highly on different ethical considerations:privacy,consent,and non-discrimination principles during AI model developments under limited conditions.Besides,innovative technologies are investigated through the paper in aspects that need implementation by incorporating transfer learning,few-shot learning,and data augmentation to adapt models so they could fit effective use processes in low-resource settings.This thus emphasizes the need for collaborative frameworks and sound methodologies that ensure applicability and fairness,tackling the technical and ethical challenges associated with data scarcity in AI.This article also discusses prospective approaches to dealing with data scarcity,emphasizing the blend of synthetic data and traditional models and the use of advanced machine learning techniques such as transfer learning and few-shot learning.These techniques aim to enhance the flexibility and effectiveness of AI systems across various industries while ensuring sustainable AI technology development amid ongoing data scarcity.展开更多
Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantage...Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.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
Objective:To investigate the effect of 12-lead electrocardiogram and 24-hour dynamic electrocardiogram in detecting pacemaker dysfunction and changes in cardiac function indexes in patients with pacemaker implantation...Objective:To investigate the effect of 12-lead electrocardiogram and 24-hour dynamic electrocardiogram in detecting pacemaker dysfunction and changes in cardiac function indexes in patients with pacemaker implantation.Methods:A total of 136 patients with pacemaker implantation in the First Clinical Medical College of Three Gorges University,Institute of Cardiovascular Disease of Three Gorges University and Yicang Central People’s Hospital from January 2023 to December 2024 were selected as the research objects.All patients received 12-lead electrocardiogram and 24-hour holter 3–14 days after implantation.Results:The overall detection rate of various types of pacemaker dysfunction by Holter was significantly higher than that by conventional ECG(27.21%vs.5.15%,χ^(2)=24.402,P<0.001).The overall arrhythmia detection rate of Holter was significantly higher than that of conventional electrocardiogram(57.35%vs.10.29%,χ^(2)=67.277,P<0.001).The time domain indexes of heart rate variability obtained by 24-hour continuous monitoring of Holter were significantly improved compared with those of conventional electrocardiogram(P<0.05).Conclusions:Compared with 12-lead electrocardiogram,24-hour holter monitoring can more accurately detect pacemaker dysfunction and arrhythmia in patients with pacemaker implantation,and provide more comprehensive data of heart rate variability,which is helpful for clinicians to better evaluate the cardiac function of patients and adjust treatment plans.展开更多
As artificial intelligence(AI)technologies continue to transform education,fostering AI literacy among university students has become increasingly important.Despite growing interest in AI-related education,empirical r...As artificial intelligence(AI)technologies continue to transform education,fostering AI literacy among university students has become increasingly important.Despite growing interest in AI-related education,empirical research on university students’AI literacy,particularly in the Chinese higher education context,remains limited.This study aims to examine the current state of AI literacy among Chinese university students through a survey-based approach.Grounded in five dimensions-AI knowledge,skills,ethics,thinking,and collaboration-the study surveyed 83 undergraduate students.The results indicate that while students possess a foundational level of AI literacy,there is considerable room for development.Positive correlations among all five dimensions suggest a mutually reinforcing relationship in students’AI engagement.Specifically,students with greater awareness of AI applications tend to demonstrate stronger collaborative learning abilities,and those who recognize the interdisciplinary nature of AI show greater attentiveness to its advancements.These findings underscore the need for more integrated and dimensionally balanced AI literacy education in higher education contexts.展开更多
This letter is a commentary on the findings of Huang et al,who emphasize the prognostic value of tumor location in gastric cancer.Analyzing data from 3287 patients using Kaplan-Meier and multivariate Cox models,the au...This letter is a commentary on the findings of Huang et al,who emphasize the prognostic value of tumor location in gastric cancer.Analyzing data from 3287 patients using Kaplan-Meier and multivariate Cox models,the authors found that the tumor location correlated with patient prognosis following surgery.Patients with tumors situated nearer to the stomach’s proximal end were associated with shorter survival periods and poorer outcomes.Notably,gender-based differences in tumor markers,particularly carbohydrate antigen 72-4,further highlight the need for sex-specific influence on the tumor location.Despite increasing recognition of tumor location as a prognostic factor,its role remains unclear in clinical prediction models for various cancers.This letter highlights the potential of incorporating tumor location into artificial intelligence-based prognostic tools to enhance prognostic models.It also outlines a stepwise framework for developing these models,from retrospective training to prospective multicenter validation and clinical implementation.In addition,it addresses the technical,ethical,and interoperability challenges critical to successful real-world prognosis.展开更多
BACKGROUND Kidney and liver transplantation are two sub-specialized medical disciplines,with transplant professionals spending decades in training.While artificial intelligencebased(AI-based)tools could potentially as...BACKGROUND Kidney and liver transplantation are two sub-specialized medical disciplines,with transplant professionals spending decades in training.While artificial intelligencebased(AI-based)tools could potentially assist in everyday clinical practice,comparative assessment of their effectiveness in clinical decision-making remains limited.AIM To compare the use of ChatGPT and GPT-4 as potential tools in AI-assisted clinical practice in these challenging disciplines.METHODS In total,400 different questions tested ChatGPT’s/GPT-4 knowledge and decision-making capacity in various renal and liver transplantation concepts.Specifically,294 multiple-choice questions were derived from open-access sources,63 questions were derived from published open-access case reports,and 43 from unpublished cases of patients treated at our department.The evaluation covered a plethora of topics,including clinical predictors,treatment options,and diagnostic criteria,among others.RESULTS ChatGPT correctly answered 50.3%of the 294 multiple-choice questions,while GPT-4 demonstrated a higher performance,answering 70.7%of questions(P<0.001).Regarding the 63 questions from published cases,ChatGPT achieved an agreement rate of 50.79%and partial agreement of 17.46%,while GPT-4 demonstrated an agreement rate of 80.95%and partial agreement of 9.52%(P=0.01).Regarding the 43 questions from unpublished cases,ChatGPT demonstrated an agreement rate of 53.49%and partial agreement of 23.26%,while GPT-4 demonstrated an agreement rate of 72.09%and partial agreement of 6.98%(P=0.004).When factoring by the nature of the task for all cases,notably,GPT-4 demonstrated outstanding performance,providing a differential diagnosis that included the final diagnosis in 90%of the cases(P=0.008),and successfully predicting the prognosis of the patient in 100%of related questions(P<0.001).CONCLUSION GPT-4 consistently provided more accurate and reliable clinical recommendations with higher percentages of full agreements both in renal and liver transplantation compared with ChatGPT.Our findings support the potential utility of AI models like ChatGPT and GPT-4 in AI-assisted clinical practice as sources of accurate,individualized medical information and facilitating decision-making.The progression and refinement of such AI-based tools could reshape the future of clinical practice,making their early adoption and adaptation by physicians a necessity.展开更多
Poyang Lake,China's largest freshwater lake,is a critical wintering ground for most of the global Siberian Grane(Grus leucogeranus)population.However,increasingly prolonged dry seasons have degraded the natural we...Poyang Lake,China's largest freshwater lake,is a critical wintering ground for most of the global Siberian Grane(Grus leucogeranus)population.However,increasingly prolonged dry seasons have degraded the natural wetlands of Poyang Lake,forcing Siberian Cranes to shift to artificial habitats.From 2015 to 2023,field surveys revealed a substantial increase in the number of Siberian Cranes in artificial habitats,with peak counts reaching 3000individuals,accounting for up to 53%of the species'global population.Satellite telemetry of 13 individuals further confirmed the spatial use of these habitats,highlighting their consistent reliance on artificial sites over multiple years.Seven high-use hotspots were identified outside of Poyang Lake,including two artificial provisioning sites that supported dense foraging flocks for extended periods.Satellite telemetry confirmed this trend,with artificial habitats making up to 64.2%of the occurrence sites in some years.This reliance on artificial habitats was closely linked to the reduced tuber biomass in natural wetlands and low winter water levels in Poyang Lake,which collectively explained 83%of the variance in crane abundance in artificial habitats.Artificial habitat use peaked in December and January,indicating marked seasonal variation.Siberian Cranes also exhibited a pronounced circadian rhythm,foraging in artificial habitats during the day and returning to natural wetlands to roost at night.Despite the shift toward artificial habitats,natural wetlands remain critical for nighttime refuge.The continued dependence on artificial habitats raises concerns about disease transmission owing to dense congregations.Conservation strategies should prioritize both the careful management of artificial provisioning sites and the restoration of natural wetlands to improve food and habitat availability within natural ecosystems,ultimately enabling the return of Siberian Cranes to their traditional natural habitats.展开更多
This review comprehensively analyzes advancements in artificial intelligence,particularly machine learning and deep learning,in medical imaging,focusing on their transformative role in enhancing diagnostic accuracy.Ou...This review comprehensively analyzes advancements in artificial intelligence,particularly machine learning and deep learning,in medical imaging,focusing on their transformative role in enhancing diagnostic accuracy.Our in-depth analysis of 138 selected studies reveals that artificial intelligence(AI)algorithms frequently achieve diagnostic performance comparable to,and often surpassing,that of human experts,excelling in complex pattern recognition.Key findings include earlier detection of conditions like skin cancer and diabetic retinopathy,alongside radiologist-level performance for pneumonia detection on chest X-rays.These technologies profoundly transform imaging by significantly improving processes in classification,segmentation,and sequential analysis across diversemodalities such as X-rays,Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound.Specific advancements with Convolutional Neural Networks,Recurrent Neural Networks,and ensemble learning techniques have facilitated more precise diagnosis,prediction,and therapy planning.Notably,Generative Adversarial Networks address limited data through augmentation,while transfer learning efficiently adapts models for scarce labeled datasets,and Reinforcement Learning shows promise in optimizing treatment protocols,collectively advancing patient care.Methodologically,a systematic review(2015-2024)used Scopus and Web of Science databases,yielding 7982 initial records.Of these,1189 underwent bibliometric analysis using the R package‘Bibliometrix’,and 138 were comprehensively reviewed for specific findings.Research output surged over the decade,led by Institute of Electrical and Electronics Engineers(IEEE)Access(19.1%).China dominates publication volume(36.1%),while the United States of America(USA)leads total citations(5605),and Hong Kong exhibits the highest average(55.60).Challenges include rigorous validation,regulatory clarity,and fostering clinician trust.This study highlights significant emerging trends and crucial future research directions for successful AI implementation in healthcare.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
Colorectal cancer is a major cause of cancer-related mortality worldwide,under-scoring the importance of early and effective colorectal cancer screening to im-prove survival rates.Traditional colorectal cancer screeni...Colorectal cancer is a major cause of cancer-related mortality worldwide,under-scoring the importance of early and effective colorectal cancer screening to im-prove survival rates.Traditional colorectal cancer screening methods include non-invasive tests,such as the fecal immunochemical test(FIT),as well as diagnostic procedures like colonoscopy.Colonoscopy remains the gold standard for detec-ting and treating precancerous polyps and early-stage cancer,regardless of whe-ther it is used as the first screening test or the second test following a positive FIT.However,its effectiveness can be affected by factors such as operator skill,patient variability,and limited lesion visibility,resulting in a significant rate of missed lesion rates and highlighting the need for more efficient and accurate screening techniques.This review is aimed to assess the current challenges of traditional screening methods with the impact of artificial intelligence(AI)in the diagnostic flow.The literature on AI-powered tools for colorectal cancer screening,including novel applications,emerging programs,and recent guidelines,has been reviewed to highlight both the advantages and limitations of implementing this technology in healthcare.Recent advances in AI have introduced soft AI colonoscopy,with the purpose of improving lesion recognition(computer-aided detection)and/or improving optical diagnosis(computer-aided diagnosis).AI-powered colono-scopy systems employ deep learning algorithms to analyze real-time endoscopic images,enhancing detection rates for adenomas,serrated lesions and cancer by reducing human error.AI-assisted colonoscopy enhances adenoma detection,enabling earlier intervention and improved patient outcomes.The benefits are particularly pronounced for less-experienced practitioners,as the detection rates for AI-assisted colonoscopy are similar to experts.AI integration also helps in the teaching process,in developing standardized procedures,and improving screening procedure accuracy and efficiency across different healthcare providers.However,there are challenges and limitations,such as the cost of AI implementation,data privacy concerns,and the need for extensive clinical validation.As AI technology continues to evolve,its transformation of the colorectal cancer screening system could revolutionize the field,making early detection more accessible and reducing mortality,on the condition that the above issues are addressed before widespread use.展开更多
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
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 The gold standard for colorectal polyp screening is currently colonoscopy,but the miss rate is still high and the adenoma detection rate and polyp detection rate are still low.The risk factors include the p...BACKGROUND The gold standard for colorectal polyp screening is currently colonoscopy,but the miss rate is still high and the adenoma detection rate and polyp detection rate are still low.The risk factors include the patient,operators,and the tools used.The use of artificial intelligence(AI)in colonoscopy has gained popularity by assisting endoscopists in the detection and characterization of polyps.AIM To evaluate the diagnostic performance of AI-assisted colonoscopy[computer assisted diagnosis(CAD)eye function]for colorectal polyp characterization.METHODS This study used a cross-sectional design conducted at the Gastrointestinal Endoscopy Center of Dr.Cipto Mangunkusumo Hospital in January-May 2024 on adult patients with suspected colorectal polyps.RESULTS A total of 60 patients with 100 polyps were involved in this study.Based on the results of the examination,it was found that the AI CAD eye function examination had a sensitivity of 79.17%,specificity of 75.00%,positive predictive value(PPV)of 89.06%,negative predictive value(NPV)of 58.33%,and accuracy of 78.00%.In polyps with diminutive size,sensitivity was 86.27%,specificity was 60.00%,PPV was 95.65%,NPV was 30.00%,and accuracy was 83.93%.Meanwhile,in polyps with non-diminutive size,sensitivity was 61.90%,specificity was 78.26%,PPV was 72.22%,NPV was 69.23%,and accuracy was 70.45%.In polyps on the left side of the colon,sensitivity was 78.85%,specificity was 81.25%,PPV was 93.18%,NPV was 54.17%,and accuracy was 79.41%.Meanwhile,in rightsided polyps the sensitivity was 80.00%,specificity was 66.67%,PPV was 80.00%,NPV was 66.67%,and accuracy was 75.00%.In sessile polyps the sensitivity was 81.54%,specificity was 50.00%,PPV was 91.38%,NPV was 29.41%,and accuracy was 77.33%.Meanwhile,in non-sessile polyps,the sensitivity was 57.14%,specificity was 88.89%,PPV was 66.67%,NPV was 84.21%,and accuracy was 80.00%.CONCLUSION AI CAD eye function examination had a high sensitivity value in diminutive,sessile polyps and right-sided polyps and a high specificity in non-diminutive,non-sessile polyps and left-sided polyps.展开更多
Inflammatory bowel disease(IBD)represents a major global health concern,signi-ficantly impacting patient quality of life and healthcare systems.Mucosal and his-tological healing have emerged as key therapeutic targets...Inflammatory bowel disease(IBD)represents a major global health concern,signi-ficantly impacting patient quality of life and healthcare systems.Mucosal and his-tological healing have emerged as key therapeutic targets,offering better long-term outcomes compared with previous targets.However,accurate disease asse-ssment remains challenging because of interobserver variability and inconsis-tencies between endoscopic and histological findings.Artificial intelligence(AI)is transforming IBD care by enhancing the precision and reproducibility of disease evaluation.This review provided a structured synthesis of AI applications in IBD,organized by diagnostic,histological,and therapeutic domains,and highlighted comparative model performance such as machine learning classifiers(random forest,support vector machine)and deep learning models(convolutional and recurrent neural networks)with reported accuracy between 80%and 97%and areas under the curve ranging from 0.74 to 0.99.Beyond summarizing existing tools,the review emphasized the ability of AI to reduce diagnostic variability,improve early prediction of therapeutic response,and streamline clinical work-flows.These advancements support a shift toward personalized treatment strate-gies and more efficient care delivery.Additionally,we outlined the expanding role of AI in clinical trials in which it supports patient stratification,endpoint prediction,and automated data integration.展开更多
In the current era of digitalization sweeping the education field,primary school English education is facing new challenges and opportunities of deep integration with artificial intelligence.This study focuses on prim...In the current era of digitalization sweeping the education field,primary school English education is facing new challenges and opportunities of deep integration with artificial intelligence.This study focuses on primary school English teachers and uses various methods such as questionnaire surveys,visits,and interviews to conduct an in-depth exploration of their artificial intelligence literacy.After data analysis,optimization strategies are proposed to further improve the artificial intelligence literacy of primary school English teachers and promote the development of educational soft power.展开更多
基金Thework and the contributions were supported by the project SV4502261/SP2022/98‘Biomedical Engineering systems XVIII’.
文摘The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary.This paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker’s functionality.In this work,we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming environment.The electrical model of the heart allows signals generation(right atrium,right ventricle)and the monitoring of the stimulation pulses.The LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in real-time.The results demonstrate the capability of proposed system to evaluate the paced and sensed pulses.The proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing mode.In addition,the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.
文摘Objective: To analyze the effect of continuous nursing on self-care ability and quality of life of patients with permanent artificial pacemaker implantation. Methods: A total of 90 patients receiving permanent artificial pacemaker treatment in our hospital during the first 8 months of November 2021 were selected as samples to compare the data differences between the two groups (control group and intervention group were 45 patients /n = 45 patients/group, respectively). Results: The compliance of the intervention group was higher than that of the control group (P;There was no significant difference in EDCA score between groups before intervention (P>0.05). After intervention, EDCA score in the intervention group was higher than that in the control group (P<0.05). The nursing quality score of intervention group was higher than that of control group (P;The compliance score of intervention group was higher than that of control group (P<0.05). After intervention, SRSS and MNA scores in the intervention group were higher than those in the control group (P<0.05). The scores of the four items in the intervention group were higher than those in the control group (P < 0.05). Discussion: Continuous nursing intervention can effectively improve the quality and effect of nursing, and has significant application value.
文摘Artificial intelligence(AI)is a new arena for human technological development,and one of the most concerning global governance issues at present.In recent years,breakthroughs in generative AI technologies have been made,and the prospects of large-scale application of AI technologies have become ever brighter,bringing us closer to the artificial general intelligence(AGI)that can enable machines to think and act like humans.As a strategic technology leading a new round of technological revolution and industrial transformation,AI offers enormous opportunities to advance human society,yet it also introduces significant security risks and challenges.How to maximize the development potential of AI at the global level while establishing an effective international governance framework has become a focus of global concern.
基金supported by the National Natural Science Foundation of China(Nos.U20A20209 and 62304228)the China National Postdoctoral Program for Innovative Talents(No.BX2021326)+3 种基金the China Postdoctoral Science Foundation(No.2021M703310)the Zhejiang Provincial Natural Science Foundation of China(No.LQ22F040003)the Ningbo Natural Science Foundation of China(No.2023J356)the State Key Laboratory for Environment-Friendly Energy Materials(No.20kfhg09).
文摘With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution.Neuromorphic computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI technology.Artificial neurons and synapses are the two core components of neuromorphic computing systems.Artificial perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research.This work reviews recent advances in artificial sensory neurons and their applications.First,biological sensory neurons are briefly described.Then,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working mechanisms.Next,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and smell.Finally,challenges faced by artificial sensory neurons at both device and system levels are summarized.
基金supported by Internal Research Support Program(IRSPG202202).
文摘Dealing with data scarcity is the biggest challenge faced by Artificial Intelligence(AI),and it will be interesting to see how we overcome this obstacle in the future,but for now,“THE SHOW MUST GO ON!!!”As AI spreads and transforms more industries,the lack of data is a significant obstacle:the best methods for teaching machines how real-world processes work.This paper explores the considerable implications of data scarcity for the AI industry,which threatens to restrict its growth and potential,and proposes plausible solutions and perspectives.In addition,this article focuses highly on different ethical considerations:privacy,consent,and non-discrimination principles during AI model developments under limited conditions.Besides,innovative technologies are investigated through the paper in aspects that need implementation by incorporating transfer learning,few-shot learning,and data augmentation to adapt models so they could fit effective use processes in low-resource settings.This thus emphasizes the need for collaborative frameworks and sound methodologies that ensure applicability and fairness,tackling the technical and ethical challenges associated with data scarcity in AI.This article also discusses prospective approaches to dealing with data scarcity,emphasizing the blend of synthetic data and traditional models and the use of advanced machine learning techniques such as transfer learning and few-shot learning.These techniques aim to enhance the flexibility and effectiveness of AI systems across various industries while ensuring sustainable AI technology development amid ongoing data scarcity.
基金supported by the Key-Area Research and Development Program of Guangdong Province(Grants No.2021B0909060002)National Natural Science Foundation of China(Grants No.62204219,62204140)Major Program of Natural Science Foundation of Zhejiang Province(Grants No.LDT23F0401).
文摘Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power consumption.To fully leverage its advantages,convertingthe external analog signals to spikes is an essential prerequisite.Conventionalapproaches including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area costs.Recent efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory system.They can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory systems.This review summarizesand benchmarks the recent progress of artificial sensory neurons.It starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial sensoryneurons.Furthermore,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also provided.Finally,we highlight the challenges and perspectives for the future development of artificial sensory neurons.
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.
文摘Objective:To investigate the effect of 12-lead electrocardiogram and 24-hour dynamic electrocardiogram in detecting pacemaker dysfunction and changes in cardiac function indexes in patients with pacemaker implantation.Methods:A total of 136 patients with pacemaker implantation in the First Clinical Medical College of Three Gorges University,Institute of Cardiovascular Disease of Three Gorges University and Yicang Central People’s Hospital from January 2023 to December 2024 were selected as the research objects.All patients received 12-lead electrocardiogram and 24-hour holter 3–14 days after implantation.Results:The overall detection rate of various types of pacemaker dysfunction by Holter was significantly higher than that by conventional ECG(27.21%vs.5.15%,χ^(2)=24.402,P<0.001).The overall arrhythmia detection rate of Holter was significantly higher than that of conventional electrocardiogram(57.35%vs.10.29%,χ^(2)=67.277,P<0.001).The time domain indexes of heart rate variability obtained by 24-hour continuous monitoring of Holter were significantly improved compared with those of conventional electrocardiogram(P<0.05).Conclusions:Compared with 12-lead electrocardiogram,24-hour holter monitoring can more accurately detect pacemaker dysfunction and arrhythmia in patients with pacemaker implantation,and provide more comprehensive data of heart rate variability,which is helpful for clinicians to better evaluate the cardiac function of patients and adjust treatment plans.
文摘As artificial intelligence(AI)technologies continue to transform education,fostering AI literacy among university students has become increasingly important.Despite growing interest in AI-related education,empirical research on university students’AI literacy,particularly in the Chinese higher education context,remains limited.This study aims to examine the current state of AI literacy among Chinese university students through a survey-based approach.Grounded in five dimensions-AI knowledge,skills,ethics,thinking,and collaboration-the study surveyed 83 undergraduate students.The results indicate that while students possess a foundational level of AI literacy,there is considerable room for development.Positive correlations among all five dimensions suggest a mutually reinforcing relationship in students’AI engagement.Specifically,students with greater awareness of AI applications tend to demonstrate stronger collaborative learning abilities,and those who recognize the interdisciplinary nature of AI show greater attentiveness to its advancements.These findings underscore the need for more integrated and dimensionally balanced AI literacy education in higher education contexts.
基金Supported by Natural Science Foundation of the Science and Technology Commission of Shanghai Municipality,No.23ZR1458300Key Discipline Project of Shanghai Municipal Health System,No.2024ZDXK0004+1 种基金Doctoral Innovation Talent Base Project for Diagnosis and Treatment of Chronic Liver Diseases,No.RCJD2021B02Pujiang Project of Shanghai Magnolia Talent Plan,No.24PJD098.
文摘This letter is a commentary on the findings of Huang et al,who emphasize the prognostic value of tumor location in gastric cancer.Analyzing data from 3287 patients using Kaplan-Meier and multivariate Cox models,the authors found that the tumor location correlated with patient prognosis following surgery.Patients with tumors situated nearer to the stomach’s proximal end were associated with shorter survival periods and poorer outcomes.Notably,gender-based differences in tumor markers,particularly carbohydrate antigen 72-4,further highlight the need for sex-specific influence on the tumor location.Despite increasing recognition of tumor location as a prognostic factor,its role remains unclear in clinical prediction models for various cancers.This letter highlights the potential of incorporating tumor location into artificial intelligence-based prognostic tools to enhance prognostic models.It also outlines a stepwise framework for developing these models,from retrospective training to prospective multicenter validation and clinical implementation.In addition,it addresses the technical,ethical,and interoperability challenges critical to successful real-world prognosis.
文摘BACKGROUND Kidney and liver transplantation are two sub-specialized medical disciplines,with transplant professionals spending decades in training.While artificial intelligencebased(AI-based)tools could potentially assist in everyday clinical practice,comparative assessment of their effectiveness in clinical decision-making remains limited.AIM To compare the use of ChatGPT and GPT-4 as potential tools in AI-assisted clinical practice in these challenging disciplines.METHODS In total,400 different questions tested ChatGPT’s/GPT-4 knowledge and decision-making capacity in various renal and liver transplantation concepts.Specifically,294 multiple-choice questions were derived from open-access sources,63 questions were derived from published open-access case reports,and 43 from unpublished cases of patients treated at our department.The evaluation covered a plethora of topics,including clinical predictors,treatment options,and diagnostic criteria,among others.RESULTS ChatGPT correctly answered 50.3%of the 294 multiple-choice questions,while GPT-4 demonstrated a higher performance,answering 70.7%of questions(P<0.001).Regarding the 63 questions from published cases,ChatGPT achieved an agreement rate of 50.79%and partial agreement of 17.46%,while GPT-4 demonstrated an agreement rate of 80.95%and partial agreement of 9.52%(P=0.01).Regarding the 43 questions from unpublished cases,ChatGPT demonstrated an agreement rate of 53.49%and partial agreement of 23.26%,while GPT-4 demonstrated an agreement rate of 72.09%and partial agreement of 6.98%(P=0.004).When factoring by the nature of the task for all cases,notably,GPT-4 demonstrated outstanding performance,providing a differential diagnosis that included the final diagnosis in 90%of the cases(P=0.008),and successfully predicting the prognosis of the patient in 100%of related questions(P<0.001).CONCLUSION GPT-4 consistently provided more accurate and reliable clinical recommendations with higher percentages of full agreements both in renal and liver transplantation compared with ChatGPT.Our findings support the potential utility of AI models like ChatGPT and GPT-4 in AI-assisted clinical practice as sources of accurate,individualized medical information and facilitating decision-making.The progression and refinement of such AI-based tools could reshape the future of clinical practice,making their early adoption and adaptation by physicians a necessity.
基金supported by the National Natural Science Foundation of China(No.32260275)Fundamental Research Funds of CAF(CAFYBB2024ZA033)。
文摘Poyang Lake,China's largest freshwater lake,is a critical wintering ground for most of the global Siberian Grane(Grus leucogeranus)population.However,increasingly prolonged dry seasons have degraded the natural wetlands of Poyang Lake,forcing Siberian Cranes to shift to artificial habitats.From 2015 to 2023,field surveys revealed a substantial increase in the number of Siberian Cranes in artificial habitats,with peak counts reaching 3000individuals,accounting for up to 53%of the species'global population.Satellite telemetry of 13 individuals further confirmed the spatial use of these habitats,highlighting their consistent reliance on artificial sites over multiple years.Seven high-use hotspots were identified outside of Poyang Lake,including two artificial provisioning sites that supported dense foraging flocks for extended periods.Satellite telemetry confirmed this trend,with artificial habitats making up to 64.2%of the occurrence sites in some years.This reliance on artificial habitats was closely linked to the reduced tuber biomass in natural wetlands and low winter water levels in Poyang Lake,which collectively explained 83%of the variance in crane abundance in artificial habitats.Artificial habitat use peaked in December and January,indicating marked seasonal variation.Siberian Cranes also exhibited a pronounced circadian rhythm,foraging in artificial habitats during the day and returning to natural wetlands to roost at night.Despite the shift toward artificial habitats,natural wetlands remain critical for nighttime refuge.The continued dependence on artificial habitats raises concerns about disease transmission owing to dense congregations.Conservation strategies should prioritize both the careful management of artificial provisioning sites and the restoration of natural wetlands to improve food and habitat availability within natural ecosystems,ultimately enabling the return of Siberian Cranes to their traditional natural habitats.
文摘This review comprehensively analyzes advancements in artificial intelligence,particularly machine learning and deep learning,in medical imaging,focusing on their transformative role in enhancing diagnostic accuracy.Our in-depth analysis of 138 selected studies reveals that artificial intelligence(AI)algorithms frequently achieve diagnostic performance comparable to,and often surpassing,that of human experts,excelling in complex pattern recognition.Key findings include earlier detection of conditions like skin cancer and diabetic retinopathy,alongside radiologist-level performance for pneumonia detection on chest X-rays.These technologies profoundly transform imaging by significantly improving processes in classification,segmentation,and sequential analysis across diversemodalities such as X-rays,Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound.Specific advancements with Convolutional Neural Networks,Recurrent Neural Networks,and ensemble learning techniques have facilitated more precise diagnosis,prediction,and therapy planning.Notably,Generative Adversarial Networks address limited data through augmentation,while transfer learning efficiently adapts models for scarce labeled datasets,and Reinforcement Learning shows promise in optimizing treatment protocols,collectively advancing patient care.Methodologically,a systematic review(2015-2024)used Scopus and Web of Science databases,yielding 7982 initial records.Of these,1189 underwent bibliometric analysis using the R package‘Bibliometrix’,and 138 were comprehensively reviewed for specific findings.Research output surged over the decade,led by Institute of Electrical and Electronics Engineers(IEEE)Access(19.1%).China dominates publication volume(36.1%),while the United States of America(USA)leads total citations(5605),and Hong Kong exhibits the highest average(55.60).Challenges include rigorous validation,regulatory clarity,and fostering clinician trust.This study highlights significant emerging trends and crucial future research directions for successful AI implementation in healthcare.
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.
文摘Colorectal cancer is a major cause of cancer-related mortality worldwide,under-scoring the importance of early and effective colorectal cancer screening to im-prove survival rates.Traditional colorectal cancer screening methods include non-invasive tests,such as the fecal immunochemical test(FIT),as well as diagnostic procedures like colonoscopy.Colonoscopy remains the gold standard for detec-ting and treating precancerous polyps and early-stage cancer,regardless of whe-ther it is used as the first screening test or the second test following a positive FIT.However,its effectiveness can be affected by factors such as operator skill,patient variability,and limited lesion visibility,resulting in a significant rate of missed lesion rates and highlighting the need for more efficient and accurate screening techniques.This review is aimed to assess the current challenges of traditional screening methods with the impact of artificial intelligence(AI)in the diagnostic flow.The literature on AI-powered tools for colorectal cancer screening,including novel applications,emerging programs,and recent guidelines,has been reviewed to highlight both the advantages and limitations of implementing this technology in healthcare.Recent advances in AI have introduced soft AI colonoscopy,with the purpose of improving lesion recognition(computer-aided detection)and/or improving optical diagnosis(computer-aided diagnosis).AI-powered colono-scopy systems employ deep learning algorithms to analyze real-time endoscopic images,enhancing detection rates for adenomas,serrated lesions and cancer by reducing human error.AI-assisted colonoscopy enhances adenoma detection,enabling earlier intervention and improved patient outcomes.The benefits are particularly pronounced for less-experienced practitioners,as the detection rates for AI-assisted colonoscopy are similar to experts.AI integration also helps in the teaching process,in developing standardized procedures,and improving screening procedure accuracy and efficiency across different healthcare providers.However,there are challenges and limitations,such as the cost of AI implementation,data privacy concerns,and the need for extensive clinical validation.As AI technology continues to evolve,its transformation of the colorectal cancer screening system could revolutionize the field,making early detection more accessible and reducing mortality,on the condition that the above issues are addressed before widespread use.
文摘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.
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 The gold standard for colorectal polyp screening is currently colonoscopy,but the miss rate is still high and the adenoma detection rate and polyp detection rate are still low.The risk factors include the patient,operators,and the tools used.The use of artificial intelligence(AI)in colonoscopy has gained popularity by assisting endoscopists in the detection and characterization of polyps.AIM To evaluate the diagnostic performance of AI-assisted colonoscopy[computer assisted diagnosis(CAD)eye function]for colorectal polyp characterization.METHODS This study used a cross-sectional design conducted at the Gastrointestinal Endoscopy Center of Dr.Cipto Mangunkusumo Hospital in January-May 2024 on adult patients with suspected colorectal polyps.RESULTS A total of 60 patients with 100 polyps were involved in this study.Based on the results of the examination,it was found that the AI CAD eye function examination had a sensitivity of 79.17%,specificity of 75.00%,positive predictive value(PPV)of 89.06%,negative predictive value(NPV)of 58.33%,and accuracy of 78.00%.In polyps with diminutive size,sensitivity was 86.27%,specificity was 60.00%,PPV was 95.65%,NPV was 30.00%,and accuracy was 83.93%.Meanwhile,in polyps with non-diminutive size,sensitivity was 61.90%,specificity was 78.26%,PPV was 72.22%,NPV was 69.23%,and accuracy was 70.45%.In polyps on the left side of the colon,sensitivity was 78.85%,specificity was 81.25%,PPV was 93.18%,NPV was 54.17%,and accuracy was 79.41%.Meanwhile,in rightsided polyps the sensitivity was 80.00%,specificity was 66.67%,PPV was 80.00%,NPV was 66.67%,and accuracy was 75.00%.In sessile polyps the sensitivity was 81.54%,specificity was 50.00%,PPV was 91.38%,NPV was 29.41%,and accuracy was 77.33%.Meanwhile,in non-sessile polyps,the sensitivity was 57.14%,specificity was 88.89%,PPV was 66.67%,NPV was 84.21%,and accuracy was 80.00%.CONCLUSION AI CAD eye function examination had a high sensitivity value in diminutive,sessile polyps and right-sided polyps and a high specificity in non-diminutive,non-sessile polyps and left-sided polyps.
文摘Inflammatory bowel disease(IBD)represents a major global health concern,signi-ficantly impacting patient quality of life and healthcare systems.Mucosal and his-tological healing have emerged as key therapeutic targets,offering better long-term outcomes compared with previous targets.However,accurate disease asse-ssment remains challenging because of interobserver variability and inconsis-tencies between endoscopic and histological findings.Artificial intelligence(AI)is transforming IBD care by enhancing the precision and reproducibility of disease evaluation.This review provided a structured synthesis of AI applications in IBD,organized by diagnostic,histological,and therapeutic domains,and highlighted comparative model performance such as machine learning classifiers(random forest,support vector machine)and deep learning models(convolutional and recurrent neural networks)with reported accuracy between 80%and 97%and areas under the curve ranging from 0.74 to 0.99.Beyond summarizing existing tools,the review emphasized the ability of AI to reduce diagnostic variability,improve early prediction of therapeutic response,and streamline clinical work-flows.These advancements support a shift toward personalized treatment strate-gies and more efficient care delivery.Additionally,we outlined the expanding role of AI in clinical trials in which it supports patient stratification,endpoint prediction,and automated data integration.
文摘In the current era of digitalization sweeping the education field,primary school English education is facing new challenges and opportunities of deep integration with artificial intelligence.This study focuses on primary school English teachers and uses various methods such as questionnaire surveys,visits,and interviews to conduct an in-depth exploration of their artificial intelligence literacy.After data analysis,optimization strategies are proposed to further improve the artificial intelligence literacy of primary school English teachers and promote the development of educational soft power.
