This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to...This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
BACKGROUND Gastrointestinal stromal tumors(GISTs)are rare mesenchymal neoplasms primarily originating in the stomach or small intestine.Duodenal GISTs are particularly uncommon,accounting for only a small fraction of ...BACKGROUND Gastrointestinal stromal tumors(GISTs)are rare mesenchymal neoplasms primarily originating in the stomach or small intestine.Duodenal GISTs are particularly uncommon,accounting for only a small fraction of GIST cases.These tumors often present with nonspecific symptoms,making early detection challenging.This case discusses a duodenal GIST misdiagnosed as pancreatic cancer due to obstructive jaundice.CASE SUMMARY A 40-year-old male with jaundice and abdominal symptoms underwent imaging,which suggested a malignant periampullary tumor.Preoperative misdiagnosis of pancreatic cancer was made,and surgery was performed.Postoperative histopathology confirmed a duodenal GIST.The role of artificial intelligence in the diagnostic pathway is explored,emphasizing its potential to differentiate between duodenal GISTs and other similar conditions using advanced imaging analysis.CONCLUSION Artificial intelligence in radiomic imaging holds significant promise in enhancing the diagnostic process for rare cancers like duodenal GISTs,ensuring timely and accurate treatment.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of de...Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of developing medical AI,there may arise not only legal risks such as infringement of privacy rights and health rights but also ethical risks stemming from violations of the principles of beneficence and non-maleficence.Methods:To effectively address the damages caused by MAI in the future,it is necessary to establish a hierarchical governance system with MAI.This paper examines the systematic collection of local practices in China and the induction and integration of legal remedies for the damage of MAI.Results:To effectively address the ethical and legal challenges of medical artificial intelligence,a hierarchical regulatory system should be established,which based on the impact of intervention measures on natural rights and differences in intervention timing.This paper finally obtains a legal hierarchical governance system corresponding to the ethical risks and legal risks of MAI in China.Conclusion:The Chinese government has formed a multi-agent governance system based on the impact of risks on rights and the timing of legal intervention,which provides a reference for other countries to follow up on the research on MAI risk management.展开更多
Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This...Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This paper presents a scoping review offering a novel perspective on the intersection of healthy behaviors,mental health,and AI literacy.By examining how individuals’understanding of AI influences their choices regarding nutrition and their susceptibility to mental health issues,the current study explores emerging trends in health behavior decision-making.This emphasizes the need for integrating AI literacy into mental health and health behaviors education,as well as the development of AI-driven tools to support healthier behavior choices.It highlights that individuals with low AI literacy may misinterpret or overly depend on AI guidance,resulting in maladaptive health choices,while those with high AI literacy may be more likely to engage reflectively and sustain positive behaviors.The paper outlines the importance of inclusive education,user-centered design,and community-based support systems to enhance AI literacy for digitally marginalized groups.AI literacy may be positioned as a key determinant of health equity,better allowing for interdisciplinary strategies that empower individuals to make informed,autonomous decisions that promote both physical and mental health.展开更多
Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosupp...Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.展开更多
Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to s...Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers.Artificial intelligence(AI)enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare.This minireview summarizes recent advancements in AI applications for FGIDs,highlighting progress in diagnostic accuracy,subtype classification,personalized interventions,and preventive strategies inspired by the traditional Chinese medicine concept of“treating the undiseased”.Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis,refining FD neuro-gastrointestinal subtyping,and screening for GERD-related complications.Moreover,AI supports dietary,psychological,and integrative medicine-based interventions to improve patient adherence and quality of life.Nonetheless,key challenges remain,including data heterogeneity,limited model interpretability,and the need for robust clinical validation.Future directions emphasize interdisciplinary collaboration,the development of multimodal and explainable AI models,and the creation of patientcentered platforms to facilitate a shift from reactive treatment to proactive prevention.This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.展开更多
To enhance the electrochemical performance of lithium-ion battery anodes with higher silicon content,it is essential to engineer their microstructure for better lithium-ion transport and mitigated volume change as wel...To enhance the electrochemical performance of lithium-ion battery anodes with higher silicon content,it is essential to engineer their microstructure for better lithium-ion transport and mitigated volume change as well.Herein,we suggest an effective approach to control the micropore structure of silicon oxide(SiO_(x))/artificial graphite(AG)composite electrodes using a perforated current collector.The electrode features a unique pore structure,where alternating high-porosity domains and low-porosity domains markedly reduce overall electrode resistance,leading to a 20%improvement in rate capability at a 5C-rate discharge condition.Using microstructure-resolved modeling and simulations,we demonstrate that the patterned micropore structure enhances lithium-ion transport,mitigating the electrolyte concentration gradient of lithium-ion.Additionally,perforating current collector with a chemical etching process increases the number of hydrogen bonding sites and enlarges the interface with the SiO_(x)/AG composite electrode,significantly improving adhesion strength.This,in turn,suppresses mechanical degradation and leads to a 50%higher capacity retention.Thus,regularly arranged micropore structure enabled by the perforated current collector successfully improves both rate capability and cycle life in SiO_(x)/AG composite electrodes,providing valuable insights into electrode engineering.展开更多
The effects of artificial aging(T6)on the creep resistance with tensile stresses in the range of 50−80 MPa at 175℃were investigated for an extruded Mg−1.22Al−0.31Ca−0.44Mn(wt.%)alloy.The Guinier-Preston(G.P.)zones pr...The effects of artificial aging(T6)on the creep resistance with tensile stresses in the range of 50−80 MPa at 175℃were investigated for an extruded Mg−1.22Al−0.31Ca−0.44Mn(wt.%)alloy.The Guinier-Preston(G.P.)zones primarily precipitate in the sample aged at 200℃for 1 h(T6-200℃/1h),while the Al_(2)Ca phases mainly precipitate in the sample aged at 275℃for 8 h(T6-275℃/8h).The T6-200℃/1h sample exhibits excellent creep resistance,with a steady-state creep rate one order of magnitude lower than that of the T6-275℃/8h sample.The abnormally high stress exponent(~8.2)observed in the T6-200℃/1h sample is associated with the power-law breakdown mechanism.TEM analysis illuminates that the creep mechanism for the T6-200℃/1h sample is cross-slip between basal and prismatic dislocations,while the T6-275℃/8h sample exhibits a mixed mechanism of dislocation cross-slip and climb.Compared with the Al_(2)Ca phase,the dense G.P.zones effectively impede dislocation climb and glide during the creep process,demonstrating superior creep resistance of the T6-200℃/1h sample.展开更多
Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparame...Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparametric ultrasound(US)techniques to provide more accurate,objective,and non-invasive evaluations of liver fibrosis and steatosis.Analyzing large datasets from US images,AI enhances diagnostic precision,enabling better quantification of liver stiffness and fat content,which are essential for diagnosing and staging liver fibrosis and steatosis.Combining advanced US modalities,such as elastography and doppler imaging with AI,has demonstrated improved sensitivity in identifying different stages of liver disease and distinguishing various degrees of steatotic liver.These advancements also contribute to greater reproducibility and reduced operator dependency,addressing some of the limitations of traditional methods.The clinical implications of AI in liver disease are vast,ranging from early detection to predicting disease progression and evaluating treatment response.Despite these promising developments,challenges such as the need for large-scale datasets,algorithm transparency,and clinical validation remain.The aim of this review is to explore the current applications and future potential of AI in liver fibrosis and steatosis assessment using multiparametric US,highlighting the technological advances and clinical relevance of this emerging field.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
Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the...Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the development of new strategies for early diagnosis and treatment is essential to improve patient outcomes.Over the past decade,the integration of artificial intelligence(AI)into gastroenterology has led to transformative advancements in medical practice.AI represents a major step towards personalized medicine,offering the potential to enhance diagnostic accuracy,refine prognostic assessments,and optimize treatment strategies.Its applications are rapidly expanding.This article explores the emerging role of AI in the management of MASLD,emphasizing its ability to improve clinical prediction,enhance the diagnostic performance of imaging modalities,and support histopathological confirmation.Additionally,it examines the development of AI-guided personalized treatments,where lifestyle modifications and close monitoring play a pivotal role in achieving therapeutic success.展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.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
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
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
In the era of artificial intelligence(AI),healthcare and medical sciences are inseparable from different AI technologies[1].ChatGPT once shocked the medical field,but the latest AI model DeepSeek has recently taken th...In the era of artificial intelligence(AI),healthcare and medical sciences are inseparable from different AI technologies[1].ChatGPT once shocked the medical field,but the latest AI model DeepSeek has recently taken the lead[2].PubMed indexed publications on DeepSeek are evolving[3],but limited to editorials and news articles.In this Letter,we explore the use of DeepSeek in early symptoms recognition for stroke care.To the best of our knowledge,this is the first DeepSeek-related writing on stroke.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
基金Supported by Japan Society for the Promotion of Science,No.24K11935.
文摘This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
文摘BACKGROUND Gastrointestinal stromal tumors(GISTs)are rare mesenchymal neoplasms primarily originating in the stomach or small intestine.Duodenal GISTs are particularly uncommon,accounting for only a small fraction of GIST cases.These tumors often present with nonspecific symptoms,making early detection challenging.This case discusses a duodenal GIST misdiagnosed as pancreatic cancer due to obstructive jaundice.CASE SUMMARY A 40-year-old male with jaundice and abdominal symptoms underwent imaging,which suggested a malignant periampullary tumor.Preoperative misdiagnosis of pancreatic cancer was made,and surgery was performed.Postoperative histopathology confirmed a duodenal GIST.The role of artificial intelligence in the diagnostic pathway is explored,emphasizing its potential to differentiate between duodenal GISTs and other similar conditions using advanced imaging analysis.CONCLUSION Artificial intelligence in radiomic imaging holds significant promise in enhancing the diagnostic process for rare cancers like duodenal GISTs,ensuring timely and accurate treatment.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金funded by China Law Society 2025 Annual Legal Research,Project grant number:CLS(2025)Y04.
文摘Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of developing medical AI,there may arise not only legal risks such as infringement of privacy rights and health rights but also ethical risks stemming from violations of the principles of beneficence and non-maleficence.Methods:To effectively address the damages caused by MAI in the future,it is necessary to establish a hierarchical governance system with MAI.This paper examines the systematic collection of local practices in China and the induction and integration of legal remedies for the damage of MAI.Results:To effectively address the ethical and legal challenges of medical artificial intelligence,a hierarchical regulatory system should be established,which based on the impact of intervention measures on natural rights and differences in intervention timing.This paper finally obtains a legal hierarchical governance system corresponding to the ethical risks and legal risks of MAI in China.Conclusion:The Chinese government has formed a multi-agent governance system based on the impact of risks on rights and the timing of legal intervention,which provides a reference for other countries to follow up on the research on MAI risk management.
文摘Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This paper presents a scoping review offering a novel perspective on the intersection of healthy behaviors,mental health,and AI literacy.By examining how individuals’understanding of AI influences their choices regarding nutrition and their susceptibility to mental health issues,the current study explores emerging trends in health behavior decision-making.This emphasizes the need for integrating AI literacy into mental health and health behaviors education,as well as the development of AI-driven tools to support healthier behavior choices.It highlights that individuals with low AI literacy may misinterpret or overly depend on AI guidance,resulting in maladaptive health choices,while those with high AI literacy may be more likely to engage reflectively and sustain positive behaviors.The paper outlines the importance of inclusive education,user-centered design,and community-based support systems to enhance AI literacy for digitally marginalized groups.AI literacy may be positioned as a key determinant of health equity,better allowing for interdisciplinary strategies that empower individuals to make informed,autonomous decisions that promote both physical and mental health.
文摘Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.
基金Supported by The Natural Science Foundation of China,No.82374292the Plans for Major Provincial Science and Technology Projects of Anhui Province,No.202303a07020003the Innovation Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine,No.ZYYCXTD-C-202401.
文摘Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers.Artificial intelligence(AI)enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare.This minireview summarizes recent advancements in AI applications for FGIDs,highlighting progress in diagnostic accuracy,subtype classification,personalized interventions,and preventive strategies inspired by the traditional Chinese medicine concept of“treating the undiseased”.Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis,refining FD neuro-gastrointestinal subtyping,and screening for GERD-related complications.Moreover,AI supports dietary,psychological,and integrative medicine-based interventions to improve patient adherence and quality of life.Nonetheless,key challenges remain,including data heterogeneity,limited model interpretability,and the need for robust clinical validation.Future directions emphasize interdisciplinary collaboration,the development of multimodal and explainable AI models,and the creation of patientcentered platforms to facilitate a shift from reactive treatment to proactive prevention.This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.NRF-2021M3H4A1A02048529)the Ministry of Trade,Industry and Energy(MOTIE)of the Korean government under grant No.RS-2022-00155854support from the DGIST Supercomputing and Big Data Center.
文摘To enhance the electrochemical performance of lithium-ion battery anodes with higher silicon content,it is essential to engineer their microstructure for better lithium-ion transport and mitigated volume change as well.Herein,we suggest an effective approach to control the micropore structure of silicon oxide(SiO_(x))/artificial graphite(AG)composite electrodes using a perforated current collector.The electrode features a unique pore structure,where alternating high-porosity domains and low-porosity domains markedly reduce overall electrode resistance,leading to a 20%improvement in rate capability at a 5C-rate discharge condition.Using microstructure-resolved modeling and simulations,we demonstrate that the patterned micropore structure enhances lithium-ion transport,mitigating the electrolyte concentration gradient of lithium-ion.Additionally,perforating current collector with a chemical etching process increases the number of hydrogen bonding sites and enlarges the interface with the SiO_(x)/AG composite electrode,significantly improving adhesion strength.This,in turn,suppresses mechanical degradation and leads to a 50%higher capacity retention.Thus,regularly arranged micropore structure enabled by the perforated current collector successfully improves both rate capability and cycle life in SiO_(x)/AG composite electrodes,providing valuable insights into electrode engineering.
基金supported by the National Natural Science Foundation of China (Nos. 52175322, 52271031)the Natural Science Foundation of Jilin Province, China (No. SKL202302015)。
文摘The effects of artificial aging(T6)on the creep resistance with tensile stresses in the range of 50−80 MPa at 175℃were investigated for an extruded Mg−1.22Al−0.31Ca−0.44Mn(wt.%)alloy.The Guinier-Preston(G.P.)zones primarily precipitate in the sample aged at 200℃for 1 h(T6-200℃/1h),while the Al_(2)Ca phases mainly precipitate in the sample aged at 275℃for 8 h(T6-275℃/8h).The T6-200℃/1h sample exhibits excellent creep resistance,with a steady-state creep rate one order of magnitude lower than that of the T6-275℃/8h sample.The abnormally high stress exponent(~8.2)observed in the T6-200℃/1h sample is associated with the power-law breakdown mechanism.TEM analysis illuminates that the creep mechanism for the T6-200℃/1h sample is cross-slip between basal and prismatic dislocations,while the T6-275℃/8h sample exhibits a mixed mechanism of dislocation cross-slip and climb.Compared with the Al_(2)Ca phase,the dense G.P.zones effectively impede dislocation climb and glide during the creep process,demonstrating superior creep resistance of the T6-200℃/1h sample.
文摘Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparametric ultrasound(US)techniques to provide more accurate,objective,and non-invasive evaluations of liver fibrosis and steatosis.Analyzing large datasets from US images,AI enhances diagnostic precision,enabling better quantification of liver stiffness and fat content,which are essential for diagnosing and staging liver fibrosis and steatosis.Combining advanced US modalities,such as elastography and doppler imaging with AI,has demonstrated improved sensitivity in identifying different stages of liver disease and distinguishing various degrees of steatotic liver.These advancements also contribute to greater reproducibility and reduced operator dependency,addressing some of the limitations of traditional methods.The clinical implications of AI in liver disease are vast,ranging from early detection to predicting disease progression and evaluating treatment response.Despite these promising developments,challenges such as the need for large-scale datasets,algorithm transparency,and clinical validation remain.The aim of this review is to explore the current applications and future potential of AI in liver fibrosis and steatosis assessment using multiparametric US,highlighting the technological advances and clinical relevance of this emerging field.
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.
文摘Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the development of new strategies for early diagnosis and treatment is essential to improve patient outcomes.Over the past decade,the integration of artificial intelligence(AI)into gastroenterology has led to transformative advancements in medical practice.AI represents a major step towards personalized medicine,offering the potential to enhance diagnostic accuracy,refine prognostic assessments,and optimize treatment strategies.Its applications are rapidly expanding.This article explores the emerging role of AI in the management of MASLD,emphasizing its ability to improve clinical prediction,enhance the diagnostic performance of imaging modalities,and support histopathological confirmation.Additionally,it examines the development of AI-guided personalized treatments,where lifestyle modifications and close monitoring play a pivotal role in achieving therapeutic success.
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
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.
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 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.
文摘In the era of artificial intelligence(AI),healthcare and medical sciences are inseparable from different AI technologies[1].ChatGPT once shocked the medical field,but the latest AI model DeepSeek has recently taken the lead[2].PubMed indexed publications on DeepSeek are evolving[3],but limited to editorials and news articles.In this Letter,we explore the use of DeepSeek in early symptoms recognition for stroke care.To the best of our knowledge,this is the first DeepSeek-related writing on stroke.
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.
