Recent Advances in Artificial Sensory Neurons:Biological Fundamentals,Devices,Applications,and Challenges

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.

Single-cell pan-omics, environmental neurology, and artificial intelligence:the time for holistic brain health research

The brain,with its trillions of neural connections,different cellular types,and molecular complexities,presents a formidable challenge for researchers aiming to comprehend the multifaceted nature of neural health.As traditional methods have provided valuable insights,emerging technologies offer unprecedented opportunities to delve deeper into the underpinnings of brain function.In the everevolving landscape of neuroscience,the quest to unravel the mysteries of the human brain is bound to take a leap forward thanks to new technological improvements and bold interpretative frameworks.

Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning

Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.

Recognition and quality mapping of traditional herbal drugs:way forward towards artificial intelligence

The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.

Gallbladder carcinoma in the era of artificial intelligence: Early diagnosis for better treatment

Gallbladder carcinoma(GBC)is the most common malignant tumor of biliary tract,with poor prognosis due to its aggressive nature and limited therapeutic options.Early detection of GBC is a major challenge,with most GBCs being detected accidentally during cholecystectomy procedures for gallbladder stones.This letter comments on the recent article by Deqing et al in the World Journal of Gastrointestinal Oncology,which summarized the various current methods used in early diagnosis of GBC,including endoscopic ultrasound(EUS)examination of the gallbladder for high-risk GBC patients,and the use of EUS-guided elasto-graphy,contrast-enhanced EUS,trans-papillary biopsy,natural orifice translu-minal endoscopic surgery,magnifying endoscopy,choledochoscopy,and confocal laser endomicroscopy when necessary for early diagnosis of GBC.However,there is a need for novel methods for early GBC diagnosis,such as the use of artificial intelligence and non-coding RNA biomarkers for improved screening protocols.Additionally,the use of in vitro and animal models may provide critical insights for advancing early detection and treatment strategies of this aggressive tumor.

Harnessing artificial intelligence for identifying conflicts of interest in research

This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.

Artificial sensory neurons and their applications

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.

Diabetes mellitus and glymphatic dysfunction:Roles for oxidative stress,mitochondria,circadian rhythm,artificial intelligence,and imaging

Diabetes mellitus(DM)is a debilitating disorder that impacts all systems of the body and has been increasing in prevalence throughout the globe.DM represents a significant clinical challenge to care for individuals and prevent the onset of chronic disability and ultimately death.Underlying cellular mechanisms for the onset and development of DM are multi-factorial in origin and involve pathways associated with the production of reactive oxygen species and the generation of oxidative stress as well as the dysfunction of mitochondrial cellular organelles,programmed cell death,and circadian rhythm impairments.These pathways can ultimately involve failure in the glymphatic pathway of the brain that is linked to circadian rhythms disorders during the loss of metabolic homeostasis.New studies incorporate a number of promising techniques to examine patients with metabolic disorders that can include machine learning and artificial intelligence pathways to potentially predict the onset of metabolic dysfunction.

Multimodal artificial intelligence system for detecting a small esophageal high-grade squamous intraepithelial neoplasia: A case report

BACKGROUND Recent advancements in artificial intelligence(AI)have significantly enhanced the capabilities of endoscopic-assisted diagnosis for gastrointestinal diseases.AI has shown great promise in clinical practice,particularly for diagnostic support,offering real-time insights into complex conditions such as esophageal squamous cell carcinoma.CASE SUMMARY In this study,we introduce a multimodal AI system that successfully identified and delineated a small and flat carcinoma during esophagogastroduodenoscopy,highlighting its potential for early detection of malignancies.The lesion was confirmed as high-grade squamous intraepithelial neoplasia,with pathology results supporting the AI system’s accuracy.The multimodal AI system offers an integrated solution that provides real-time,accurate diagnostic information directly within the endoscopic device interface,allowing for single-monitor use without disrupting endoscopist’s workflow.CONCLUSION This work underscores the transformative potential of AI to enhance endoscopic diagnosis by enabling earlier,more accurate interventions.

Neurotransmitter-mediated artificial synapses based on organic electrochemical transistors for future biomimic and bioinspired neuromorphic systems

Organic electrochemical transistors have emerged as a solution for artificial synapses that mimic the neural functions of the brain structure,holding great potentials to break the bottleneck of von Neumann architectures.However,current artificial synapses rely primarily on electrical signals,and little attention has been paid to the vital role of neurotransmitter-mediated artificial synapses.Dopamine is a key neurotransmitter associated with emotion regulation and cognitive processes that needs to be monitored in real time to advance the development of disease diagnostics and neuroscience.To provide insights into the development of artificial synapses with neurotransmitter involvement,this review proposes three steps towards future biomimic and bioinspired neuromorphic systems.We first summarize OECT-based dopamine detection devices,and then review advances in neurotransmitter-mediated artificial synapses and resultant advanced neuromorphic systems.Finally,by exploring the challenges and opportunities related to such neuromorphic systems,we provide a perspective on the future development of biomimetic and bioinspired neuromorphic systems.

Leveraging Artificial Intelligence to Achieve Sustainable Public Healthcare Services in Saudi Arabia: A Systematic Literature Review of Critical Success Factors

This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technology deployment relevant to disease management,healthcare delivery,epidemiology,and policy-making.However,its AI is culturally sensitive and ethically grounded in Islam.Based on the PRISMA framework,an SLR evaluated primary academic literature,cases,and practices of Saudi Arabia’s AI implementation in the public healthcare sector.Instead,it categorizes prior research based on how AI can work,the issues it poses,and its implications for the Kingdom’s healthcare system.The Saudi Arabian context analyses show that AI has increased the discreet prediction of diseases,resource management,and monitoring outbreaks during mass congregations such as hajj.Therefore,the study outlines critical areas for defining the potential for artificial intelligence and areas for enhancing digital development to support global healthcare progress.The key themes emerging from the review include Saudi Arabia:(i)the effectiveness of AI with human interaction for sustainable health services;(ii)conditions and quality control to enhance the quality of health care services using AI;(iii)environmental factors as influencing factors for public health care;(iv)Artificial Intelligence,and advanced decision-making technology for Middle Eastern health care systems.For policymakers,healthcare managers,and researchers who will engage with AI innovation,the review proclaims that AI applications should respect the country’s socio-cultural and ethical practices and pave the way for sustainable healthcare provision.More empirical research is needed on the implementation issues with AI,creating culturally appropriate models of AI,and finding new applications of AI to address the increasing demand for healthcare services in Saudi Arabia.

Artificial intelligence-driven strategies for managing renal and urinary complications in inflammatory bowel disease

In this editorial,we discuss the article by Singh et al published in World Journal of Nephrology,stating the need for timely adjustments in inflammatory bowel disease(IBD)patients'long-term management plans.IBD is chronic and lifelong,with recurrence and remission cycles,including ulcerative colitis and Crohn's disease.It's exact etiology is unknown but likely multifactorial.Related to gut flora and immune issues.Besides intestinal symptoms,IBD can also affect various extrain-testinal manifestations such as those involving the skin,joints,eyes and urinary system.The anatomical proximity of urinary system waste disposal to that of the alimentary canal makes early detection and the differentiation of such symptoms very difficult.Various studies show that IBD and it's first-line drugs have nephro-toxicity,impacting the patients'life quality.Existing guidelines give very few references for kidney lesion monitoring.Singh et al's plan aims to improve treatment management for IBD patients with glomerular filtration rate decline,specifically those at risk.Most of IBD patients are young and they need lifelong therapy.So early therapy cessation,taking into account drug side effects,can be helpful.Artificial intelligence-driven diagnosis and treatment has a big potential for management improvements in IBD and other chronic diseases.

Artificial intelligence and the impact of multiomics on the reporting of case reports

The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022,with AI research tripling during this period.Multiomics fields,including genomics and proteomics,also advanced,exemplified by the Human Proteome Project achieving a 90%complete blueprint by 2021.This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting.A review of studies and case reports was conducted to evaluate AI and multiomics integration.Key areas analyzed included diagnostic accuracy,predictive modeling,and personalized treatment approaches driven by AI tools.Case examples were studied to assess impacts on clinical decision-making.AI and multiomics enhanced data integration,predictive insights,and treatment personalization.Fields like radiomics,genomics,and proteomics improved diagnostics and guided therapy.For instance,the“AI radiomics,geno-mics,oncopathomics,and surgomics project”combined radiomics and genomics for surgical decision-making,enabling preoperative,intraoperative,and post-operative interventions.AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data.AI and multiomics enable standardized data analysis,dynamic updates,and predictive modeling in case reports.Traditional reports often lack objectivity,but AI enhances reproducibility and decision-making by processing large datasets.Challenges include data standardization,biases,and ethical concerns.Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine.AI and multiomics integration is revolutionizing clinical research and practice.Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential.Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.

Predictability Study of Weather and Climate Events Related to Artificial Intelligence Models

Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.

Toward a Learnable Climate Model in the Artificial Intelligence Era

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

Concept of Artificial Intelligence (AI) and Its Use in Orthopaedic Practice: Applications and Pitfalls: A Narrative Review

Background: The growth and use of Artificial Intelligence (AI) in the medical field is rapidly rising. AI is exhibiting a practical tool in the healthcare industry in patient care. The objective of this current review is to assess and analyze the use of AI and its use in orthopedic practice, as well as its applications, limitations, and pitfalls. Methods: A review of all relevant databases such as EMBASE, Cochrane Database of Systematic Reviews, MEDLINE, Science Citation Index, Scopus, and Web of Science with keywords of AI, orthopedic surgery, applications, and drawbacks. All related articles on AI and orthopaedic practice were reviewed. A total of 3210 articles were included in the review. Results: The data from 351 studies were analyzed where in orthopedic surgery. AI is being used for diagnostic procedures, radiological diagnosis, models of clinical care, and utilization of hospital and bed resources. AI has also taken a chunk of share in assisted robotic orthopaedic surgery. Conclusions: AI has now become part of the orthopedic practice and will further increase its stake in the healthcare industry. Nonetheless, clinicians should remain aware of AI’s serious limitations and pitfalls and consider the drawbacks and errors in its use.

Status of clinical applications of artificial intelligence in echocardiography

Artificial intelligence(AI)technology has been increasingly used in medical field with its rapid developments.Echocardiography is one of the best imaging methods for clinical diagnosis of heart diseases,and combining with AI could further improve its diagnostic efficiency.Though the applications of AI in echocardiography remained at a relatively early stage,a variety of automated quantitative and analytical techniques were rapidly emerging and initially entered clinical practice.The status of clinical applications of AI in echocardiography were reviewed in this article.

Artificial Intelligence and Computer Vision during Surgery: Discussing Laparoscopic Images with ChatGPT4—Preliminary Results

Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce. Aim: To test a novel AI commercially available tool for image analysis on a series of laparoscopic scenes. Methods: The research tools included OPENAI CHATGPT 4.0 with its corresponding image recognition plugin which was fed with a list of 100 laparoscopic selected snapshots from common surgical procedures. In order to score reliability of received responses from image-recognition bot, two corresponding scales were developed ranging from 0 - 5. The set of images was divided into two groups: unlabeled (Group A) and labeled (Group B), and according to the type of surgical procedure or image resolution. Results: AI was able to recognize correctly the context of surgical-related images in 97% of its reports. For the labeled surgical pictures, the image-processing bot scored 3.95/5 (79%), whilst for the unlabeled, it scored 2.905/5 (58.1%). Phases of the procedure were commented in detail, after all successful interpretations. With rates 4 - 5/5, the chatbot was able to talk in detail about the indications, contraindications, stages, instrumentation, complications and outcome rates of the operation discussed. Conclusion: Interaction between surgeon and chatbot appears to be an interesting frontend for further research by clinicians in parallel with evolution of its complex underlying infrastructure. In this early phase of using artificial intelligence for image recognition in surgery, no safe conclusions can be drawn by small cohorts with commercially available software. Further development of medically-oriented AI software and clinical world awareness are expected to bring fruitful information on the topic in the years to come.

A quantitative study of disruptive technology policy texts:An example of China’s artificial intelligence policy

Purpose:The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention.This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence(AI)disruptive technology policy,and to put forward suggestions for optimizing China’s AI disruptive technology policy.Design/methodology/approach:Develop a three-dimensional analytical framework for“policy tools-policy actors-policy themes”and apply policy tools,social network analysis,and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools,cooperative relationships among policy actors,and the trends in policy theme settings within China’s innovative AI technology policy.Findings:We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close.Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects,forming a“center-periphery”network structure.Policy tool usage is predominantly focused on supply and environmental types,with a severe inadequacy in demand-side policy tool utilization.Policy themes are diverse,encompassing topics such as“Intelligent Services”“Talent Cultivation”“Information Security”and“Technological Innovation”,which will remain focal points.Under the themes of“Intelligent Services”and“Intelligent Governance”,policy tool usage is relatively balanced,with close collaboration among policy entities.However,the theme of“AI Theoretical System”lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities.Research limitations:The data sources and experimental scope are subject to certain limitations,potentially introducing biases and imperfections into the research results,necessitating further validation and refinement.Practical implications:The study introduces a three-dimensional analysis framework for disruptive technology policy texts,which is significant for formulating and enhancing disruptive technology policies.Originality/value:This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts.It systematically evaluates China’s AI policies quantitatively,focusing on policy tools,policy actors,policy themes.The study uncovers the characteristics and deficiencies of current AI policies,offering recommendations for formulating and enhancing disruptive technology policies.