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were derived from open-access sources,63 questions were derived from published open-access case reports,and 43 from unpublished cases of patients treated at our department.The evaluation covered a plethora of topics,including clinical predictors,treatment options,and diagnostic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GPT-4 demonstrated an agreement rate of 80.95%and partial agreement of 9.52%(P=0.01).Regarding the 43 questions from unpublished cases,ChatGPT demonstrated an agreement rate of 53.49%and partial agreement of 23.26%,while GPT-4 demonstrated an agreement rate of 72.09%and partial agreement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
Antimicrobial resistance is a global public health threat,and the World Health Organization(WHO)has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed.The ...Antimicrobial resistance is a global public health threat,and the World Health Organization(WHO)has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed.The discovery and introduction of novel antibiotics are time-consuming and expensive.According to WHO’s report of antibacterial agents in clinical development,only 18 novel antibiotics have been approved since 2014.Therefore,novel antibiotics are critically needed.Artificial intelligence(AI)has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics.Here,we first summarized recently marketed novel antibiotics,and antibiotic candidates in clinical development.In addition,we systematically reviewed the involvement of AI in antibacterial drug development and utilization,including small molecules,antimicrobial peptides,phage therapy,essential oils,as well as resistance mechanism prediction,and antibiotic stewardship.展开更多
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...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.展开更多
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 transforma...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.展开更多
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 an...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.展开更多
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 ident...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.展开更多
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 recogni...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.展开更多
This analytical research paper explores the transformative impact of artificial intelligence(AI)in orthodontics,with a focus on its objectives:Identifying current applications,evaluating benefits,addressing challenges...This analytical research paper explores the transformative impact of artificial intelligence(AI)in orthodontics,with a focus on its objectives:Identifying current applications,evaluating benefits,addressing challenges,and projecting future developments.AI,a subset of computer science designed to simulate human intelligence,has seen rapid integration into orthodontic practice.The paper examines AI technologies such as machine learning,deep learning,natural language processing,computer vision,and robotics,which are increasingly used to analyze patient data,assist with diagnosis and treatment planning,automate routine tasks,and improve patient communication.AI systems offer precise malocclusion diagnoses,predict treatment outcomes,and customize treatment plans by leveraging dental imagery.They also streamline image analysis,improve diagnostic accuracy,and enhance patient engagement through personalized communication.The objectives include evaluating the benefits of AI in terms of efficiency,accuracy,and personalized care,while acknowledging the challenges like data quality,algorithm transparency,and practical implementation.Despite these hurdles,AI presents promising prospects in advanced imaging,predictive analytics,and clinical decision-making.In conclusion,AI holds the potential to revolutionize orthodontic practices by improving operational efficiency,diagnostic precision and patient outcomes.With collaborative efforts to overcome challenges,AI could play a pivotal role in advancing orthodontic care.展开更多
The rapid development of artificial intelligence (AI) technology is profoundly reshaping all walks of life, especially in the medical field. AI provides innovative tools for medical diagnosis, treatment, and managemen...The rapid development of artificial intelligence (AI) technology is profoundly reshaping all walks of life, especially in the medical field. AI provides innovative tools for medical diagnosis, treatment, and management and lays a solid foundation for personalized medicine and precision medicine. This paper reviews the latest progress in the application of AI technologies such as machine learning (ML) and deep learning (DL) in ophthalmic diseases.展开更多
This article discusses the manuscript recently published in the World Journal of Gastroenterology,which explores the application of deep learning models in decision-making processes via wireless capsule endoscopy.Inte...This article discusses the manuscript recently published in the World Journal of Gastroenterology,which explores the application of deep learning models in decision-making processes via wireless capsule endoscopy.Integrating artificial intelligence(AI)into gastrointestinal disease diagnosis represents a transformative step toward precision medicine,enhancing real-time accuracy in detecting multi-category lesions at earlier stages,including small bowel lesions and precancerous polyps,ultimately improving patient outcomes.However,the use of AI in clinical settings raises ethical considerations that extend beyond technological potential.Issues of patient privacy,data security,and potential diagnostic biases require careful attention.AI models must prioritize diverse and representative datasets to mitigate inequities and ensure diagnostic accuracy across populations.Furthermore,balancing AI with clinical expertise is crucial,positioning AI as a supportive tool rather than a replacement for physician judgment.Addressing these ethical challenges will support the responsible deployment of AI,through equitable contribution to patient-centered care.展开更多
Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic dat...Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis,thus providing strong support for personalized treatments.In radiomics,AI can analyze high-dimensional features from computed tomography(CT),magnetic resonance imaging(MRI),and positron emission tomography/computed tomography(PET/CT)images to discover imaging biomarkers associated with tumor heterogeneity,treatment response,and disease progression,thereby enabling non-invasive,real-time assessments for personalized therapy.Pathomics leverages AI for deep analysis of digital pathology images,and can uncover subtle changes in tissue microenvironments,cellular characteristics,and morphological features,and offer unique insights into immunotherapy response prediction and biomarker discovery.These AI-driven technologies not only enhance the speed,accuracy,and robustness of biomarker discovery but also significantly improve the precision,personalization,and effectiveness of clinical treatments,and are driving a shift from empirical to precision medicine.Despite challenges such as data quality,model interpretability,integration of multi-modal data,and privacy protection,the ongoing advancements in AI,coupled with interdisciplinary collaboration,are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction.These improvements are expected to lead to more accurate,personalized treatment strategies and ultimately better patient outcomes,marking a significant step forward in the evolution of precision medicine.展开更多
The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and em...The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and employment generation.The transformative impact of Artificial Intelligence(AI)has revolutionised multiple facets of the automotive industry,encompassing intelligent manufacturing processes,diagnostic systems,control mechanisms,supply chain operations,customer service platforms,and traffic management solutions.While extensive research exists on the above aspects of AI applications in automotive contexts,there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research.This review introduces a novel taxonomic framework that provides a holistic perspective on AI integration into the automotive sector,focusing on next-generation AI methods and their critical implementation aspects.Additionally,the proposed conceptual framework for real-time condition monitoring of electric vehicle subsystems delivers actionable maintenance recommendations to stakeholders,addressing a critical gap in the field.The review highlights that AI has significantly expedited the development of autonomous vehicles regarding navigation,decision-making,and safety features through the use of advanced algorithms and deep learning structures.Furthermore,it identifies advanced driver assistance systems,vehicle health monitoring,and predictive maintenance as the most impactful AI applications,transforming operational safety and maintenance efficiency in modern automotive technologies.The work is beneficial to understanding the various use cases of AI in the different automotive domains,where AI maintains a state-of-the-art for sector-specific applications,providing a strong foundation for meeting Industry 4.0 needs and encouraging AI use among more nascent industry segments.The current work is intended to consolidate previous works while shedding some light on future research directions in promoting further growth of AI-based innovations in the scope of automotive applications.展开更多
This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technol...This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technology deployment relevant to disease management,healthcare delivery,epidemiology,and policy-making.However,its AI is culturally sensitive and ethically grounded in Islam.Based on the PRISMA framework,an SLR evaluated primary academic literature,cases,and practices of Saudi Arabia’s AI implementation in the public healthcare sector.Instead,it categorizes prior research based on how AI can work,the issues it poses,and its implications for the Kingdom’s healthcare system.The Saudi Arabian context analyses show that AI has increased the discreet prediction of diseases,resource management,and monitoring outbreaks during mass congregations such as hajj.Therefore,the study outlines critical areas for defining the potential for artificial intelligence and areas for enhancing digital development to support global healthcare progress.The key themes emerging from the review include Saudi Arabia:(i)the effectiveness of AI with human interaction for sustainable health services;(ii)conditions and quality control to enhance the quality of health care services using AI;(iii)environmental factors as influencing factors for public health care;(iv)Artificial Intelligence,and advanced decision-making technology for Middle Eastern health care systems.For policymakers,healthcare managers,and researchers who will engage with AI innovation,the review proclaims that AI applications should respect the country’s socio-cultural and ethical practices and pave the way for sustainable healthcare provision.More empirical research is needed on the implementation issues with AI,creating culturally appropriate models of AI,and finding new applications of AI to address the increasing demand for healthcare services in Saudi Arabia.展开更多
Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with...Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with artificial intelligence(AI)playing a key role in analyzing complex data to improve diagnostic accuracy,predict outcomes,and optimize therapies.AI can identify patterns in imaging and biomarkers,facilitating the earlier detection of medical conditions.Wearable devices and health applications facilitate continuous monitoring and personalized care.Emerging fields such as digital Chinese medicine offer additional perspectives by integrating traditional diagnostic principles with modern digital tools,contributing to holistic and individualized cardiovascular care.This study examines the advancements and challenges in personalized cardiovascular medicine,highlighting the need to address issues such as data privacy,algorithmic bias,and accessibility to promote the equitable application of personalized medicine.展开更多
We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered tex...We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered text summarization and text mining,we synthesize experts’narratives on sustainable development challenges and solutions in Kardüz Upland,Türkiye.We then analyze their alignment with the UN Sustainable Development Goals(SDGs)using document embedding.Investment in infrastructure,education,and resilient socio-ecological systems emerged as priority sectors to combat poor infrastructure,geographic isolation,climate change,poverty,depopulation,unemployment,low education levels,and inadequate social services.The narratives were closest in substance to SDG 1,3,and 11.Social dimensions of sustainability were more pronounced than environmental dimensions.The presented approach supports policymakers in organizing loosely structured sustainability tran sition knowledge and fragmented data corpora,while also advancing AI applications for designing and planning sustainable development policies at the regional level.展开更多
Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth...Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure.Additionally,local data processing demands substantial manpower and hardware investments.Data isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and research.In this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain technology.This system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative work.The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)format.Then,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic analysis.Then,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services.The Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.展开更多
Under the rapid impetus of artificial intelligence(AI)technology,human society is stepping into the age of intelligence at an unprecedented speed.A new generation of information technology such as AI is not only a new...Under the rapid impetus of artificial intelligence(AI)technology,human society is stepping into the age of intelligence at an unprecedented speed.A new generation of information technology such as AI is not only a new engine of economic development,but also a gas pedal of social development,which has had a profound impact on the field of education.In the face of the opportunities and challenges of the AI era,it is particularly urgent to build a scientific and reasonable education evaluation system.This paper combines the context of the times with the new technology of AI to discuss the opportunities,challenges,and implementation strategies of educational evaluation reform in the era of AI,with a view to providing references for the construction of the educational evaluation system and the development of high-quality education in the new era.展开更多
Advancements in machine learning have revolutionized preoperative risk assessment.In this article,we comment on the article by Huang et al,which presents a recent multicenter cohort study demonstrated that machine lea...Advancements in machine learning have revolutionized preoperative risk assessment.In this article,we comment on the article by Huang et al,which presents a recent multicenter cohort study demonstrated that machine learning algorithms effectively stratify recurrence-free survival,providing a robust predictive framework for maximizing surgical outcomes in intrahepatic cholangiocarcinoma.By leveraging interpretable models,the research enhances clinical decision-making,allowing for more precise patient selection and personalized surgical strategies.These findings highlight the growing role of artificial intelligence in optimizing surgical outcomes and improving prognostic accuracy in hepatobiliary oncology.展开更多
Current diagnosis of autoimmune pancreatitis(AIP)is challenging and often requires combining multiple dimensions.There is a need to explore new methods for diagnosing AIP.The development of artificial intelligence(AI)...Current diagnosis of autoimmune pancreatitis(AIP)is challenging and often requires combining multiple dimensions.There is a need to explore new methods for diagnosing AIP.The development of artificial intelligence(AI)is evident,and it is believed to have potential in the clinical diagnosis of AIP.This article aims to list the current diagnostic difficulties of AIP,describe existing AI applications,and suggest directions for future AI usages in AIP diagnosis.展开更多
BACKGROUND Artificial intelligence(AI)has become significantly integrated into healthcare,particularly in the diag-nosing of neurological disorders.This advancement has enabled neurologists and physicians to diagnose ...BACKGROUND Artificial intelligence(AI)has become significantly integrated into healthcare,particularly in the diag-nosing of neurological disorders.This advancement has enabled neurologists and physicians to diagnose conditions more quickly and effectively,ultimately benefiting patients.AIM To explore the current status and key highlights of AI-related articles in diagnosing of neurological disorders.METHODS A systematic literature review was conducted in the Web of Science Core Collection database using the following strategy:TS=("Artificial Intelligence"OR"Computational Intelligence"OR"Machine Learning"OR"AI")AND TS=("Neurological disorders"OR"CNS disorder"AND"diagnosis").The search was limited to articles and reviews.Microsoft Excel 2019 and VOSviewer were utilized to identify major contributors,including authors,institutions,countries,and journals.Additionally,VOSviewer was employed to analyze and visualize current trends and hot topics through network visualization maps.RESULTS A total of 276 publications from 2000 to 2024 were retrieved.The United States,India,and China emerged as the top contributors in this field.Major institutions included Johns Hopkins University,King's College London,and Harvard Medical School.The most prolific author was U.Rajendra Acharya from the University of Southern Queensland(Australia).Among journals,IEEE Access,Scientific Reports,and Sensors were the most productive,while Frontiers in Neuroscience led in total citations.Central topics in AI-related articles on neurological disorders diagnosis included Alzheimer's disease,Parkinson's disease,dementia,epilepsy,autism,attention deficit hyperactivity disorder,and their intersections with deep learning and AI.CONCLUSION Research on AI's role in diagnosing neurological disorders is becoming widely recognized for its growing importance.AI shows promise in diagnosing various neurological disorders,yet requires further improvement and extensive future research.展开更多
文摘BACKGROUND Kidney and liver transplantation are two sub-specialized medical disciplines,with transplant professionals spending decades in training.While artificial intelligencebased(AI-based)tools could potentially assist in everyday clinical practice,comparative assessment of their effectiveness in clinical decision-making remains limited.AIM To compare the use of ChatGPT and GPT-4 as potential tools in AI-assisted clinical practice in these challenging disciplines.METHODS In total,400 different questions tested ChatGPT’s/GPT-4 knowledge and decision-making capacity in various renal and liver transplantation concepts.Specifically,294 multiple-choice questions were derived from open-access sources,63 questions were derived from published open-access case reports,and 43 from unpublished cases of patients treated at our department.The evaluation covered a plethora of topics,including clinical predictors,treatment options,and diagnostic criteria,among others.RESULTS ChatGPT correctly answered 50.3%of the 294 multiple-choice questions,while GPT-4 demonstrated a higher performance,answering 70.7%of questions(P<0.001).Regarding the 63 questions from published cases,ChatGPT achieved an agreement rate of 50.79%and partial agreement of 17.46%,while GPT-4 demonstrated an agreement rate of 80.95%and partial agreement of 9.52%(P=0.01).Regarding the 43 questions from unpublished cases,ChatGPT demonstrated an agreement rate of 53.49%and partial agreement of 23.26%,while GPT-4 demonstrated an agreement rate of 72.09%and partial agreement of 6.98%(P=0.004).When factoring by the nature of the task for all cases,notably,GPT-4 demonstrated outstanding performance,providing a differential diagnosis that included the final diagnosis in 90%of the cases(P=0.008),and successfully predicting the prognosis of the patient in 100%of related questions(P<0.001).CONCLUSION GPT-4 consistently provided more accurate and reliable clinical recommendations with higher percentages of full agreements both in renal and liver transplantation compared with ChatGPT.Our findings support the potential utility of AI models like ChatGPT and GPT-4 in AI-assisted clinical practice as sources of accurate,individualized medical information and facilitating decision-making.The progression and refinement of such AI-based tools could reshape the future of clinical practice,making their early adoption and adaptation by physicians a necessity.
基金supported by the National Natural Science Foundation of China(32300157)the Shanghai Municipal Science and Technology Commission(19411964900)+1 种基金the Major Research and Development Project of Innovative Drugs,Ministry of Science and Technology of China(2017ZX09304005)the Wellcome Trust.
文摘Antimicrobial resistance is a global public health threat,and the World Health Organization(WHO)has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed.The discovery and introduction of novel antibiotics are time-consuming and expensive.According to WHO’s report of antibacterial agents in clinical development,only 18 novel antibiotics have been approved since 2014.Therefore,novel antibiotics are critically needed.Artificial intelligence(AI)has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics.Here,we first summarized recently marketed novel antibiotics,and antibiotic candidates in clinical development.In addition,we systematically reviewed the involvement of AI in antibacterial drug development and utilization,including small molecules,antimicrobial peptides,phage therapy,essential oils,as well as resistance mechanism prediction,and antibiotic stewardship.
文摘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.
文摘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.
基金Supported by American Diabetes AssociationAmerican Heart Association+3 种基金NIH NIEHSNIH NIANIH NINDSand NIH ARRA.
文摘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.
文摘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.
文摘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.
文摘This analytical research paper explores the transformative impact of artificial intelligence(AI)in orthodontics,with a focus on its objectives:Identifying current applications,evaluating benefits,addressing challenges,and projecting future developments.AI,a subset of computer science designed to simulate human intelligence,has seen rapid integration into orthodontic practice.The paper examines AI technologies such as machine learning,deep learning,natural language processing,computer vision,and robotics,which are increasingly used to analyze patient data,assist with diagnosis and treatment planning,automate routine tasks,and improve patient communication.AI systems offer precise malocclusion diagnoses,predict treatment outcomes,and customize treatment plans by leveraging dental imagery.They also streamline image analysis,improve diagnostic accuracy,and enhance patient engagement through personalized communication.The objectives include evaluating the benefits of AI in terms of efficiency,accuracy,and personalized care,while acknowledging the challenges like data quality,algorithm transparency,and practical implementation.Despite these hurdles,AI presents promising prospects in advanced imaging,predictive analytics,and clinical decision-making.In conclusion,AI holds the potential to revolutionize orthodontic practices by improving operational efficiency,diagnostic precision and patient outcomes.With collaborative efforts to overcome challenges,AI could play a pivotal role in advancing orthodontic care.
文摘The rapid development of artificial intelligence (AI) technology is profoundly reshaping all walks of life, especially in the medical field. AI provides innovative tools for medical diagnosis, treatment, and management and lays a solid foundation for personalized medicine and precision medicine. This paper reviews the latest progress in the application of AI technologies such as machine learning (ML) and deep learning (DL) in ophthalmic diseases.
文摘This article discusses the manuscript recently published in the World Journal of Gastroenterology,which explores the application of deep learning models in decision-making processes via wireless capsule endoscopy.Integrating artificial intelligence(AI)into gastrointestinal disease diagnosis represents a transformative step toward precision medicine,enhancing real-time accuracy in detecting multi-category lesions at earlier stages,including small bowel lesions and precancerous polyps,ultimately improving patient outcomes.However,the use of AI in clinical settings raises ethical considerations that extend beyond technological potential.Issues of patient privacy,data security,and potential diagnostic biases require careful attention.AI models must prioritize diverse and representative datasets to mitigate inequities and ensure diagnostic accuracy across populations.Furthermore,balancing AI with clinical expertise is crucial,positioning AI as a supportive tool rather than a replacement for physician judgment.Addressing these ethical challenges will support the responsible deployment of AI,through equitable contribution to patient-centered care.
基金supported by grants from the National Natural Science Foundation of China(Grant No.82272008)The Science&Technology Development Fund of Tianjin Education Commission for Higher Education(Grant No.2021KJ194)Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-009A).
文摘Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis,thus providing strong support for personalized treatments.In radiomics,AI can analyze high-dimensional features from computed tomography(CT),magnetic resonance imaging(MRI),and positron emission tomography/computed tomography(PET/CT)images to discover imaging biomarkers associated with tumor heterogeneity,treatment response,and disease progression,thereby enabling non-invasive,real-time assessments for personalized therapy.Pathomics leverages AI for deep analysis of digital pathology images,and can uncover subtle changes in tissue microenvironments,cellular characteristics,and morphological features,and offer unique insights into immunotherapy response prediction and biomarker discovery.These AI-driven technologies not only enhance the speed,accuracy,and robustness of biomarker discovery but also significantly improve the precision,personalization,and effectiveness of clinical treatments,and are driving a shift from empirical to precision medicine.Despite challenges such as data quality,model interpretability,integration of multi-modal data,and privacy protection,the ongoing advancements in AI,coupled with interdisciplinary collaboration,are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction.These improvements are expected to lead to more accurate,personalized treatment strategies and ultimately better patient outcomes,marking a significant step forward in the evolution of precision medicine.
基金The authors are grateful to the Universiti Malaysia Pahang Al-Sultan Abdullah and the Malaysian Ministry of Higher Education for their generous support and funding provided through University Distinguished Research Grants(Project No.RDU223016)as well as financial assistance provided through the Fundamental Research Grant Scheme(No.FRGS/1/2022/TK10/UMP/02/35).
文摘The automotive sector is crucial in modern society,facilitating essential transportation needs across personal,commercial,and logistical domains while significantly contributing to national economic development and employment generation.The transformative impact of Artificial Intelligence(AI)has revolutionised multiple facets of the automotive industry,encompassing intelligent manufacturing processes,diagnostic systems,control mechanisms,supply chain operations,customer service platforms,and traffic management solutions.While extensive research exists on the above aspects of AI applications in automotive contexts,there is a compelling need to synthesise this knowledge comprehensively to guide and inspire future research.This review introduces a novel taxonomic framework that provides a holistic perspective on AI integration into the automotive sector,focusing on next-generation AI methods and their critical implementation aspects.Additionally,the proposed conceptual framework for real-time condition monitoring of electric vehicle subsystems delivers actionable maintenance recommendations to stakeholders,addressing a critical gap in the field.The review highlights that AI has significantly expedited the development of autonomous vehicles regarding navigation,decision-making,and safety features through the use of advanced algorithms and deep learning structures.Furthermore,it identifies advanced driver assistance systems,vehicle health monitoring,and predictive maintenance as the most impactful AI applications,transforming operational safety and maintenance efficiency in modern automotive technologies.The work is beneficial to understanding the various use cases of AI in the different automotive domains,where AI maintains a state-of-the-art for sector-specific applications,providing a strong foundation for meeting Industry 4.0 needs and encouraging AI use among more nascent industry segments.The current work is intended to consolidate previous works while shedding some light on future research directions in promoting further growth of AI-based innovations in the scope of automotive applications.
基金funded by the Scientific ResearchDeanship at theUniversity ofHa’il-Saudi Arabia through project number-RG-23251.
文摘This review aims to analyze the development and impact of Artificial Intelligence(AI)in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives.It is extensively devoted to AI technology deployment relevant to disease management,healthcare delivery,epidemiology,and policy-making.However,its AI is culturally sensitive and ethically grounded in Islam.Based on the PRISMA framework,an SLR evaluated primary academic literature,cases,and practices of Saudi Arabia’s AI implementation in the public healthcare sector.Instead,it categorizes prior research based on how AI can work,the issues it poses,and its implications for the Kingdom’s healthcare system.The Saudi Arabian context analyses show that AI has increased the discreet prediction of diseases,resource management,and monitoring outbreaks during mass congregations such as hajj.Therefore,the study outlines critical areas for defining the potential for artificial intelligence and areas for enhancing digital development to support global healthcare progress.The key themes emerging from the review include Saudi Arabia:(i)the effectiveness of AI with human interaction for sustainable health services;(ii)conditions and quality control to enhance the quality of health care services using AI;(iii)environmental factors as influencing factors for public health care;(iv)Artificial Intelligence,and advanced decision-making technology for Middle Eastern health care systems.For policymakers,healthcare managers,and researchers who will engage with AI innovation,the review proclaims that AI applications should respect the country’s socio-cultural and ethical practices and pave the way for sustainable healthcare provision.More empirical research is needed on the implementation issues with AI,creating culturally appropriate models of AI,and finding new applications of AI to address the increasing demand for healthcare services in Saudi Arabia.
文摘Cardiovascular diseases are the leading cause of death,requiring innovative approaches for prevention,diagnosis,and treatment.Personalized medicine customizes interventions according to individual characteristics,with artificial intelligence(AI)playing a key role in analyzing complex data to improve diagnostic accuracy,predict outcomes,and optimize therapies.AI can identify patterns in imaging and biomarkers,facilitating the earlier detection of medical conditions.Wearable devices and health applications facilitate continuous monitoring and personalized care.Emerging fields such as digital Chinese medicine offer additional perspectives by integrating traditional diagnostic principles with modern digital tools,contributing to holistic and individualized cardiovascular care.This study examines the advancements and challenges in personalized cardiovascular medicine,highlighting the need to address issues such as data privacy,algorithmic bias,and accessibility to promote the equitable application of personalized medicine.
基金work conducted under COST Action CA21125-a European forum for revitalisation of marginalised moun-tain areas(MARGISTAR)supported by COST(European Cooperation in Science and Technology)gratefully acknowledges the support received for the research from the University of Ljubljana’s research program Forest,forestry and renewable forest resources(P4-0059).
文摘We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered text summarization and text mining,we synthesize experts’narratives on sustainable development challenges and solutions in Kardüz Upland,Türkiye.We then analyze their alignment with the UN Sustainable Development Goals(SDGs)using document embedding.Investment in infrastructure,education,and resilient socio-ecological systems emerged as priority sectors to combat poor infrastructure,geographic isolation,climate change,poverty,depopulation,unemployment,low education levels,and inadequate social services.The narratives were closest in substance to SDG 1,3,and 11.Social dimensions of sustainability were more pronounced than environmental dimensions.The presented approach supports policymakers in organizing loosely structured sustainability tran sition knowledge and fragmented data corpora,while also advancing AI applications for designing and planning sustainable development policies at the regional level.
基金supported by the National Natural Science Foundation of China(62122064,62331021,62371410)the Natural Science Foundation of Fujian Province of China(2023J02005 and 2021J011184)+1 种基金the President Fund of Xiamen University(20720220063)the Nanqiang Outstanding Talents Program of Xiamen University.
文摘Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large hospitals.This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure.Additionally,local data processing demands substantial manpower and hardware investments.Data isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and research.In this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain technology.This system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative work.The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD)format.Then,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic analysis.Then,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services.The Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.
文摘Under the rapid impetus of artificial intelligence(AI)technology,human society is stepping into the age of intelligence at an unprecedented speed.A new generation of information technology such as AI is not only a new engine of economic development,but also a gas pedal of social development,which has had a profound impact on the field of education.In the face of the opportunities and challenges of the AI era,it is particularly urgent to build a scientific and reasonable education evaluation system.This paper combines the context of the times with the new technology of AI to discuss the opportunities,challenges,and implementation strategies of educational evaluation reform in the era of AI,with a view to providing references for the construction of the educational evaluation system and the development of high-quality education in the new era.
文摘Advancements in machine learning have revolutionized preoperative risk assessment.In this article,we comment on the article by Huang et al,which presents a recent multicenter cohort study demonstrated that machine learning algorithms effectively stratify recurrence-free survival,providing a robust predictive framework for maximizing surgical outcomes in intrahepatic cholangiocarcinoma.By leveraging interpretable models,the research enhances clinical decision-making,allowing for more precise patient selection and personalized surgical strategies.These findings highlight the growing role of artificial intelligence in optimizing surgical outcomes and improving prognostic accuracy in hepatobiliary oncology.
基金Supported by Youth Start-up Fund of the Naval Medical University,No.2023QN052Basic Medical Research Project of the First Affiliated Hospital of Naval Medical University,No.2023QD16+2 种基金Simulated RCT Research Project of Shanghai Hospital Deveopment Center,No.SHDC2024CRI048Changhai Hospital Changfeng Talent PlanShanghai Public Health Key Discipline Project,No.GWVI-11.1-21.
文摘Current diagnosis of autoimmune pancreatitis(AIP)is challenging and often requires combining multiple dimensions.There is a need to explore new methods for diagnosing AIP.The development of artificial intelligence(AI)is evident,and it is believed to have potential in the clinical diagnosis of AIP.This article aims to list the current diagnostic difficulties of AIP,describe existing AI applications,and suggest directions for future AI usages in AIP diagnosis.
文摘BACKGROUND Artificial intelligence(AI)has become significantly integrated into healthcare,particularly in the diag-nosing of neurological disorders.This advancement has enabled neurologists and physicians to diagnose conditions more quickly and effectively,ultimately benefiting patients.AIM To explore the current status and key highlights of AI-related articles in diagnosing of neurological disorders.METHODS A systematic literature review was conducted in the Web of Science Core Collection database using the following strategy:TS=("Artificial Intelligence"OR"Computational Intelligence"OR"Machine Learning"OR"AI")AND TS=("Neurological disorders"OR"CNS disorder"AND"diagnosis").The search was limited to articles and reviews.Microsoft Excel 2019 and VOSviewer were utilized to identify major contributors,including authors,institutions,countries,and journals.Additionally,VOSviewer was employed to analyze and visualize current trends and hot topics through network visualization maps.RESULTS A total of 276 publications from 2000 to 2024 were retrieved.The United States,India,and China emerged as the top contributors in this field.Major institutions included Johns Hopkins University,King's College London,and Harvard Medical School.The most prolific author was U.Rajendra Acharya from the University of Southern Queensland(Australia).Among journals,IEEE Access,Scientific Reports,and Sensors were the most productive,while Frontiers in Neuroscience led in total citations.Central topics in AI-related articles on neurological disorders diagnosis included Alzheimer's disease,Parkinson's disease,dementia,epilepsy,autism,attention deficit hyperactivity disorder,and their intersections with deep learning and AI.CONCLUSION Research on AI's role in diagnosing neurological disorders is becoming widely recognized for its growing importance.AI shows promise in diagnosing various neurological disorders,yet requires further improvement and extensive future research.
