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
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
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
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
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
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
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
Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical h...Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical history,clinical assessment,biochemical markers,and imaging techniques,often present limitations in sensitivity and specificity,especially in atypical cases.In recent years,artificial intelligence(AI)has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning(ML)and deep learning(DL)models.This review evaluates the current applications of AI in both adult and pediatric AAp,focusing on clinical data-based models,radiological imaging analysis,and AI-assisted clinical decision support systems.ML models such as random forest,support vector machines,logistic regression,and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems,achieving sensitivity and specificity rates exceeding 90%in multiple studies.Additionally,DL techniques,particularly convolutional neural networks,have been shown to outperform radiologists in interpreting ultrasound and computed tomography images,enhancing diagnostic confidence.This review synthesized findings from 65 studies,demonstrating that AI models integrating multimodal data including clinical,laboratory,and imaging parameters further improved diagnostic precision.Moreover,explainable AI approaches,such as SHapley Additive exPlanations and local interpretable model-agnostic explanations,have facilitated model transparency,fostering clinician trust in AI-driven decision-making.This review highlights the advancements in AI for AAp diagnosis,emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases.While preliminary results are promising,further prospective,multicenter studies are required for large-scale clinical implementation,given that a great proportion of current evidence derives from retrospective designs,and existing prospective cohorts exhibit limited sample sizes or protocol variability.Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.展开更多
In this work,a general definition,meaning,and importance of engineering are expressed generally,and the main branches of engineering are briefly discussed.The concept of technology is explored,and the relationship bet...In this work,a general definition,meaning,and importance of engineering are expressed generally,and the main branches of engineering are briefly discussed.The concept of technology is explored,and the relationship between engineering and technology is briefly outlined.The relationship between artificial intelligence and engineering is examined both generally and specifically.The place of artificial intelligence within science is evaluated according to different approaches.The general approach to philosophy and philosophy of science is briefly interpreted,and the perspectives of some specific philosophers of science are compared.The relationship between artificial intelligence and philosophy of science is examined in general terms according to various approaches.The meaning and importance of philosophy of engineering and philosophy of technology are then defined according to the general approach.The next section articulates the Philosophy of Artificial Intelligence and the Artificial Intelligence of Philosophy using John McCarthy's approach,and also defines the philosophy of artificial intelligence according to this general approach.The New Philosophy Perspective is then defined by the author,and the eight basic branches of Philosophy and Hybrid Philosophy,along with their relevant theories,are briefly outlined.A new perspective has been defined for Philosophy of Science which is one of the basic branches of philosophy.Accordingly,the main sciences,branches of science,and hybrid sciences for the new basic branches of philosophy have been outlined.The new branches of science and the corresponding hierarchy of sciences,based on the broader scale of the universe,have been defined,and the ideal scientific system has been illustrated.The next section briefly outlines the relationships between old and new branches of science.Finally,the structure of some old and new branches of philosophy is examined due to the new perspective of philosophy.The reconstructions of the Philosophy of Computer Science,Philosophy of Statistics,Philosophy of Monetary Values,Philosophy of Artificial Intelligence,Philosophy of Engineering,Philosophy of Information Technologies,Philosophy of Information Law,and Philosophy of Digital Technology,Philosophy of Digital Art,Philosophy of Architecture as defined by the new philosophical perspective,are outlined.The interaction of artificial intelligence philosophy with these branches of philosophy has been generally expressed.展开更多
BACKGROUND Artificial intelligence(AI)is transforming healthcare by improving diagnostic accuracy and predictive analytics.Periodontal diseases are recognized as risk factors for systemic conditions,including type 2 d...BACKGROUND Artificial intelligence(AI)is transforming healthcare by improving diagnostic accuracy and predictive analytics.Periodontal diseases are recognized as risk factors for systemic conditions,including type 2 diabetes mellitus,cardiovascular disease,Alzheimer’s disease,polycystic ovary syndrome,thyroid dysfunction,and post-coronavirus disease 2019 complications.These conditions exhibit complex bidirectional interactions,underscoring the importance of early detection and risk stratification.Current diagnostic tools often fail to capture these interactions at an early stage,limiting timely intervention.This study hypothesizes that AI-driven approaches can significantly improve early diagnosis and risk prediction of periodontal-systemic interactions,enhancing clinical outcomes.AIM To evaluate AI’s role in diagnosing and predicting periodontal-systemic interactions in studies from 2010 to 2024.METHODS This systematic review followed PRISMA guidelines(2009)and included peerreviewed articles from PubMed,Scopus,and Embase.Studies with large sample sizes(≥500 participants)were selected,focusing on AI models integrating multiomics data and advanced imaging techniques such as cone beam computed tomography and magnetic resonance imaging.Machine learning models processed structured clinical data,deep learning models combined imaging and clinical data,and natural language processing models extracted insights from clinical notes.RESULTS AI applications significantly enhanced diagnostic and predictive accuracy,reducing diagnostic time by 40%and improving predictive accuracy by 25%in periodontal patients with type 2 diabetes mellitus.Studies with sample sizes of 1000-1500 participants reported diagnostic accuracy improvements up to 92%,with specificity and sensitivity rates of 94%and 90%,respectively.Increasing sample sizes over the years reflected advancements in AI,data collection,and model training,reinforcing model reliability.CONCLUSION AI’s integration of multi-omics and imaging data has transformed early diagnosis and risk prediction in periodontal-systemic interactions,improving clinical outcomes and decision-making.展开更多
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
The integration of artificial intelligence(AI)into the realm of robotic urologic surgery represents a remarkable paradigm shift in the field of urology and surgical healthcare.AI,with its advanced data analysis and ma...The integration of artificial intelligence(AI)into the realm of robotic urologic surgery represents a remarkable paradigm shift in the field of urology and surgical healthcare.AI,with its advanced data analysis and machine learning capabilities,has not only expedited the evolution of robotic surgical procedures but also significantly improved diagnostic accuracy and surgical outcomes.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
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
Feature selection(FS)is a pivotal pre-processing step in developing data-driven models,influencing reliability,performance and optimization.Although existing FS techniques can yield high-performance metrics for certai...Feature selection(FS)is a pivotal pre-processing step in developing data-driven models,influencing reliability,performance and optimization.Although existing FS techniques can yield high-performance metrics for certain models,they do not invariably guarantee the extraction of the most critical or impactful features.Prior literature underscores the significance of equitable FS practices and has proposed diverse methodologies for the identification of appropriate features.However,the challenge of discerning the most relevant and influential features persists,particularly in the context of the exponential growth and heterogeneity of big data—a challenge that is increasingly salient in modern artificial intelligence(AI)applications.In response,this study introduces an innovative,automated statistical method termed Farea Similarity for Feature Selection(FSFS).The FSFS approach computes a similarity metric for each feature by benchmarking it against the record-wise mean,thereby finding feature dependencies and mitigating the influence of outliers that could potentially distort evaluation outcomes.Features are subsequently ranked according to their similarity scores,with the threshold established at the average similarity score.Notably,lower FSFS values indicate higher similarity and stronger data correlations,whereas higher values suggest lower similarity.The FSFS method is designed not only to yield reliable evaluation metrics but also to reduce data complexity without compromising model performance.Comparative analyses were performed against several established techniques,including Chi-squared(CS),Correlation Coefficient(CC),Genetic Algorithm(GA),Exhaustive Approach,Greedy Stepwise Approach,Gain Ratio,and Filtered Subset Eval,using a variety of datasets such as the Experimental Dataset,Breast Cancer Wisconsin(Original),KDD CUP 1999,NSL-KDD,UNSW-NB15,and Edge-IIoT.In the absence of the FSFS method,the highest classifier accuracies observed were 60.00%,95.13%,97.02%,98.17%,95.86%,and 94.62%for the respective datasets.When the FSFS technique was integrated with data normalization,encoding,balancing,and feature importance selection processes,accuracies improved to 100.00%,97.81%,98.63%,98.94%,94.27%,and 98.46%,respectively.The FSFS method,with a computational complexity of O(fn log n),demonstrates robust scalability and is well-suited for datasets of large size,ensuring efficient processing even when the number of features is substantial.By automatically eliminating outliers and redundant data,FSFS reduces computational overhead,resulting in faster training and improved model performance.Overall,the FSFS framework not only optimizes performance but also enhances the interpretability and explainability of data-driven models,thereby facilitating more trustworthy decision-making in AI applications.展开更多
Artificial Intelligence(AI),as a strategic technology leading the new round of scientific and technological revolution and industrial transformation,is reshaping the global power dynamics.Major countries are accelerat...Artificial Intelligence(AI),as a strategic technology leading the new round of scientific and technological revolution and industrial transformation,is reshaping the global power dynamics.Major countries are accelerating their strategic deployment around technological innovation,standard-setting,and rule-building,making technology increasingly intertwined with geopolitics.Since the U.S.released Preparing for the Future of Artificial Intelligence in 2016,it has integrated AI into its national strategic system and continuously upgraded it,aiming to maintain its dominance in the global competition.Meanwhile,the internal and external tensions of the U.S.AI strategy have become increasingly prominent,profoundly influencing its global strategy and future trajectory.展开更多
Artificial intelligence(AI)has evolved at an unprecedented pace in recent years.This rapid advancement includes algorithmic breakthroughs,cross-disciplinary integration,and diverse applications—driven by growing comp...Artificial intelligence(AI)has evolved at an unprecedented pace in recent years.This rapid advancement includes algorithmic breakthroughs,cross-disciplinary integration,and diverse applications—driven by growing computational power,massive datasets,and collaborative global research.This special issue of Emerging Artificial Intelligence Technologies and Applications was conceived to provide a platformfor cuttingedge AI research communication,developing novel methodologies,cross-domain applications,and critical advancements in addressing real-world challenges.Over the past months,we have witnessed a remarkable diversity of submissions,reflecting the global trend of AI innovation.Below,we synthesize the key insights from these works,highlighting their collective contribution to advancing AI’s theoretical frontiers and practical applications.展开更多
Artificial intelligence(AI)is revolutionizing the traditional paradigm of drug development at an unprecedented pace.With the rapid advancement of technologies such as deep learning and machine learning,AI has demonstr...Artificial intelligence(AI)is revolutionizing the traditional paradigm of drug development at an unprecedented pace.With the rapid advancement of technologies such as deep learning and machine learning,AI has demonstrated substantial potential in various aspects of pharmaceutical research,including drug target identification,molecular design,and clinical trial optimization(Figure 1).Industry reports suggest that AI has the potential to reduce the drug development timeline from the conventional 10–15 years to 2–3 years,while also slashing development costs by billions of dollars.This article provides a comprehensive analysis of the current applications and future trends of AI in drug development and discovery,focusing on three dimensions:current hotspots,challenges,and future directions.展开更多
The rapid development of artificial intelligence(AI),machine learning(ML),and deep learning(DL)in recent years has transformed many sectors.A fundamental shift has occurred in approaches to solving complex problems an...The rapid development of artificial intelligence(AI),machine learning(ML),and deep learning(DL)in recent years has transformed many sectors.A fundamental shift has occurred in approaches to solving complex problems and making decisions in many different fields.These advanced technologies have enabled significant breakthroughs in sectors including entertainment,finance,transportation,and healthcare.AI systems,which can analyze vast volumes of data,have significantly driven efficiency and innovation.With remarkable accuracy,patterns can be identified and predictions generated,improving decision-making processes and facilitating the development of more intelligent solutions.The increasing adoption of these technologies by organizations has expanded the potential for AI to change processes and improve results.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
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 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 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 the National Research Foundation(NRF)grant funded by the Korean government(MSIT)(RS-2023-00211580,RS-2023-00237308).
文摘Artificial sensory systems mimic the five human senses to facilitate data interaction between the real and virtual worlds.Accurate data analysis is crucial for converting external stimuli from each artificial sense into user-relevant information,yet conventional signal processing methods struggle with the massive scale,noise,and artificial sensory systems characteristics of data generated by artificial sensory devices.Integrating artificial intelligence(AI)is essential for addressing these challenges and enhancing the performance of artificial sensory systems,making it a rapidly growing area of research in recent years.However,no studies have systematically categorized the output functions of these systems or analyzed the associated AI algorithms and data processing methods.In this review,we present a systematic overview of the latest AI techniques aimed at enhancing the cognitive capabilities of artificial sensory systems replicating the five human senses:touch,taste,vision,smell,and hearing.We categorize the AI-enabled capabilities of artificial sensory systems into four key areas:cognitive simulation,perceptual enhancement,adaptive adjustment,and early warning.We introduce specialized AI algorithms and raw data processing methods for each function,designed to enhance and optimize sensing performance.Finally,we offer a perspective on the future of AI-integrated artificial sensory systems,highlighting technical challenges and potential real-world application scenarios for further innovation.Integration of AI with artificial sensory systems will enable advanced multimodal perception,real-time learning,and predictive capabilities.This will drive precise environmental adaptation and personalized feedback,ultimately positioning these systems as foundational technologies in smart healthcare,agriculture,and automation.
基金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.
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.
文摘Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical history,clinical assessment,biochemical markers,and imaging techniques,often present limitations in sensitivity and specificity,especially in atypical cases.In recent years,artificial intelligence(AI)has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning(ML)and deep learning(DL)models.This review evaluates the current applications of AI in both adult and pediatric AAp,focusing on clinical data-based models,radiological imaging analysis,and AI-assisted clinical decision support systems.ML models such as random forest,support vector machines,logistic regression,and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems,achieving sensitivity and specificity rates exceeding 90%in multiple studies.Additionally,DL techniques,particularly convolutional neural networks,have been shown to outperform radiologists in interpreting ultrasound and computed tomography images,enhancing diagnostic confidence.This review synthesized findings from 65 studies,demonstrating that AI models integrating multimodal data including clinical,laboratory,and imaging parameters further improved diagnostic precision.Moreover,explainable AI approaches,such as SHapley Additive exPlanations and local interpretable model-agnostic explanations,have facilitated model transparency,fostering clinician trust in AI-driven decision-making.This review highlights the advancements in AI for AAp diagnosis,emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases.While preliminary results are promising,further prospective,multicenter studies are required for large-scale clinical implementation,given that a great proportion of current evidence derives from retrospective designs,and existing prospective cohorts exhibit limited sample sizes or protocol variability.Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.
文摘In this work,a general definition,meaning,and importance of engineering are expressed generally,and the main branches of engineering are briefly discussed.The concept of technology is explored,and the relationship between engineering and technology is briefly outlined.The relationship between artificial intelligence and engineering is examined both generally and specifically.The place of artificial intelligence within science is evaluated according to different approaches.The general approach to philosophy and philosophy of science is briefly interpreted,and the perspectives of some specific philosophers of science are compared.The relationship between artificial intelligence and philosophy of science is examined in general terms according to various approaches.The meaning and importance of philosophy of engineering and philosophy of technology are then defined according to the general approach.The next section articulates the Philosophy of Artificial Intelligence and the Artificial Intelligence of Philosophy using John McCarthy's approach,and also defines the philosophy of artificial intelligence according to this general approach.The New Philosophy Perspective is then defined by the author,and the eight basic branches of Philosophy and Hybrid Philosophy,along with their relevant theories,are briefly outlined.A new perspective has been defined for Philosophy of Science which is one of the basic branches of philosophy.Accordingly,the main sciences,branches of science,and hybrid sciences for the new basic branches of philosophy have been outlined.The new branches of science and the corresponding hierarchy of sciences,based on the broader scale of the universe,have been defined,and the ideal scientific system has been illustrated.The next section briefly outlines the relationships between old and new branches of science.Finally,the structure of some old and new branches of philosophy is examined due to the new perspective of philosophy.The reconstructions of the Philosophy of Computer Science,Philosophy of Statistics,Philosophy of Monetary Values,Philosophy of Artificial Intelligence,Philosophy of Engineering,Philosophy of Information Technologies,Philosophy of Information Law,and Philosophy of Digital Technology,Philosophy of Digital Art,Philosophy of Architecture as defined by the new philosophical perspective,are outlined.The interaction of artificial intelligence philosophy with these branches of philosophy has been generally expressed.
文摘BACKGROUND Artificial intelligence(AI)is transforming healthcare by improving diagnostic accuracy and predictive analytics.Periodontal diseases are recognized as risk factors for systemic conditions,including type 2 diabetes mellitus,cardiovascular disease,Alzheimer’s disease,polycystic ovary syndrome,thyroid dysfunction,and post-coronavirus disease 2019 complications.These conditions exhibit complex bidirectional interactions,underscoring the importance of early detection and risk stratification.Current diagnostic tools often fail to capture these interactions at an early stage,limiting timely intervention.This study hypothesizes that AI-driven approaches can significantly improve early diagnosis and risk prediction of periodontal-systemic interactions,enhancing clinical outcomes.AIM To evaluate AI’s role in diagnosing and predicting periodontal-systemic interactions in studies from 2010 to 2024.METHODS This systematic review followed PRISMA guidelines(2009)and included peerreviewed articles from PubMed,Scopus,and Embase.Studies with large sample sizes(≥500 participants)were selected,focusing on AI models integrating multiomics data and advanced imaging techniques such as cone beam computed tomography and magnetic resonance imaging.Machine learning models processed structured clinical data,deep learning models combined imaging and clinical data,and natural language processing models extracted insights from clinical notes.RESULTS AI applications significantly enhanced diagnostic and predictive accuracy,reducing diagnostic time by 40%and improving predictive accuracy by 25%in periodontal patients with type 2 diabetes mellitus.Studies with sample sizes of 1000-1500 participants reported diagnostic accuracy improvements up to 92%,with specificity and sensitivity rates of 94%and 90%,respectively.Increasing sample sizes over the years reflected advancements in AI,data collection,and model training,reinforcing model reliability.CONCLUSION AI’s integration of multi-omics and imaging data has transformed early diagnosis and risk prediction in periodontal-systemic interactions,improving clinical outcomes and decision-making.
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.
文摘The integration of artificial intelligence(AI)into the realm of robotic urologic surgery represents a remarkable paradigm shift in the field of urology and surgical healthcare.AI,with its advanced data analysis and machine learning capabilities,has not only expedited the evolution of robotic surgical procedures but also significantly improved diagnostic accuracy and surgical outcomes.
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.
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Feature selection(FS)is a pivotal pre-processing step in developing data-driven models,influencing reliability,performance and optimization.Although existing FS techniques can yield high-performance metrics for certain models,they do not invariably guarantee the extraction of the most critical or impactful features.Prior literature underscores the significance of equitable FS practices and has proposed diverse methodologies for the identification of appropriate features.However,the challenge of discerning the most relevant and influential features persists,particularly in the context of the exponential growth and heterogeneity of big data—a challenge that is increasingly salient in modern artificial intelligence(AI)applications.In response,this study introduces an innovative,automated statistical method termed Farea Similarity for Feature Selection(FSFS).The FSFS approach computes a similarity metric for each feature by benchmarking it against the record-wise mean,thereby finding feature dependencies and mitigating the influence of outliers that could potentially distort evaluation outcomes.Features are subsequently ranked according to their similarity scores,with the threshold established at the average similarity score.Notably,lower FSFS values indicate higher similarity and stronger data correlations,whereas higher values suggest lower similarity.The FSFS method is designed not only to yield reliable evaluation metrics but also to reduce data complexity without compromising model performance.Comparative analyses were performed against several established techniques,including Chi-squared(CS),Correlation Coefficient(CC),Genetic Algorithm(GA),Exhaustive Approach,Greedy Stepwise Approach,Gain Ratio,and Filtered Subset Eval,using a variety of datasets such as the Experimental Dataset,Breast Cancer Wisconsin(Original),KDD CUP 1999,NSL-KDD,UNSW-NB15,and Edge-IIoT.In the absence of the FSFS method,the highest classifier accuracies observed were 60.00%,95.13%,97.02%,98.17%,95.86%,and 94.62%for the respective datasets.When the FSFS technique was integrated with data normalization,encoding,balancing,and feature importance selection processes,accuracies improved to 100.00%,97.81%,98.63%,98.94%,94.27%,and 98.46%,respectively.The FSFS method,with a computational complexity of O(fn log n),demonstrates robust scalability and is well-suited for datasets of large size,ensuring efficient processing even when the number of features is substantial.By automatically eliminating outliers and redundant data,FSFS reduces computational overhead,resulting in faster training and improved model performance.Overall,the FSFS framework not only optimizes performance but also enhances the interpretability and explainability of data-driven models,thereby facilitating more trustworthy decision-making in AI applications.
文摘Artificial Intelligence(AI),as a strategic technology leading the new round of scientific and technological revolution and industrial transformation,is reshaping the global power dynamics.Major countries are accelerating their strategic deployment around technological innovation,standard-setting,and rule-building,making technology increasingly intertwined with geopolitics.Since the U.S.released Preparing for the Future of Artificial Intelligence in 2016,it has integrated AI into its national strategic system and continuously upgraded it,aiming to maintain its dominance in the global competition.Meanwhile,the internal and external tensions of the U.S.AI strategy have become increasingly prominent,profoundly influencing its global strategy and future trajectory.
文摘Artificial intelligence(AI)has evolved at an unprecedented pace in recent years.This rapid advancement includes algorithmic breakthroughs,cross-disciplinary integration,and diverse applications—driven by growing computational power,massive datasets,and collaborative global research.This special issue of Emerging Artificial Intelligence Technologies and Applications was conceived to provide a platformfor cuttingedge AI research communication,developing novel methodologies,cross-domain applications,and critical advancements in addressing real-world challenges.Over the past months,we have witnessed a remarkable diversity of submissions,reflecting the global trend of AI innovation.Below,we synthesize the key insights from these works,highlighting their collective contribution to advancing AI’s theoretical frontiers and practical applications.
基金supported by the Centrally Guided Local Science and Technology Development Project(2024ZYD0043).
文摘Artificial intelligence(AI)is revolutionizing the traditional paradigm of drug development at an unprecedented pace.With the rapid advancement of technologies such as deep learning and machine learning,AI has demonstrated substantial potential in various aspects of pharmaceutical research,including drug target identification,molecular design,and clinical trial optimization(Figure 1).Industry reports suggest that AI has the potential to reduce the drug development timeline from the conventional 10–15 years to 2–3 years,while also slashing development costs by billions of dollars.This article provides a comprehensive analysis of the current applications and future trends of AI in drug development and discovery,focusing on three dimensions:current hotspots,challenges,and future directions.
基金funded by the Research,Development,and Innovation Authority(RDIA),Kingdom of Saudi Arabia,with grant number 13382-PSU-2023-PSNU-R-3-1-EIsupported by the Automated Systems and Computing Lab(ASCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The rapid development of artificial intelligence(AI),machine learning(ML),and deep learning(DL)in recent years has transformed many sectors.A fundamental shift has occurred in approaches to solving complex problems and making decisions in many different fields.These advanced technologies have enabled significant breakthroughs in sectors including entertainment,finance,transportation,and healthcare.AI systems,which can analyze vast volumes of data,have significantly driven efficiency and innovation.With remarkable accuracy,patterns can be identified and predictions generated,improving decision-making processes and facilitating the development of more intelligent solutions.The increasing adoption of these technologies by organizations has expanded the potential for AI to change processes and improve results.
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.
