Artificial intelligence(AI)is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies,such as graphic processing unit,Internet of Things,cloud comp...Artificial intelligence(AI)is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies,such as graphic processing unit,Internet of Things,cloud computing,and the blockchain,in the new generation of big data and Industry 4.0.In this paper,we construct an extensive survey over the period 1961-2018 of AI and deep learning.The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI,from underlying mechanisms to practical applications,from fundamental algorithms to industrial achievements,from current status to future trends.Although there exist many issues toward AI,it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields.展开更多
The development of diversified energy structures,distributed energy scheduling models and active participation ability of users,leads to a rapid movement toward energy system in which different energy carriers and sys...The development of diversified energy structures,distributed energy scheduling models and active participation ability of users,leads to a rapid movement toward energy system in which different energy carriers and systems interact together in a synergistic way.This energy development will face many challenges with the requirements of big data processing capability,professional skill,distributed collaboration and realtime monitoring for the energy system that demands an intelligent and flexible tool to realize the smart energy.Artificial intelligence(AI)technology has become a focus because of its better performance.This paper proposed a classification method that incorporates the intelligence of an independent energy unit(IEU)and the intelligence among interconnected energy units(IEUS)to review the development of AI technology in energy systems.The dominant structures of IEU can be considered from three aspects including perception,decision and implementation to study the optimal strategy for AI methods utilized in IEU.And considering the interaction relationship of IEUS,the AI applied for it can be described by the coordinated relationship and adversarial relationship problems to achieve consensus.By discussing the AI technologies and the potentials of AI in the energy system,some suggestions are presented to improve intelligent technologies for sustainable energy systems in the future.展开更多
Sepsis remains a major challenge internationally for healthcare systems.Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management.In sepsis,mortality increases with e...Sepsis remains a major challenge internationally for healthcare systems.Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management.In sepsis,mortality increases with every hour left untreated.Artificial intelligence(AI)is transforming worldwide healthcare delivery at present.This review has outlined how AI can augment strategies to address this global disease burden.AI and machine learning(ML)algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods.Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs.This gives providers additional time to plan and execute treatment plans,thereby avoiding increasing complications associated with delayed diagnosis of sepsis.The potential for cost savings with AI implementation is also discussed,including improving workflow efficiencies,reducing administrative costs,and improving healthcare outcomes.Despite these advantages,clinicians have been slow to adopt AI into clinical practice.Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use.Furthermore,the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies.Finally,we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology。展开更多
Gastrointestinal(GI)complications frequently necessitate intensive care unit(ICU)admission.Additionally,critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions....Gastrointestinal(GI)complications frequently necessitate intensive care unit(ICU)admission.Additionally,critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions.However,these patients form a vulnerable group,who are at risk for developing side effects and complications.Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients.Artificial intelligence(AI)is a rapidly evolving technology with several potential applications in healthcare settings.ICUs produce a large amount of data,which may be employed for creation of AI algorithms,and provide a lucrative opportunity for application of AI.However,the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.展开更多
Several studies exist in the literature regarding the exploitation of artificial intelligence in intensive care.However,an important gap between clinical research and daily clinical practice still exists that can only...Several studies exist in the literature regarding the exploitation of artificial intelligence in intensive care.However,an important gap between clinical research and daily clinical practice still exists that can only be bridged by robust validation studies carried out by multidisciplinary teams.展开更多
Recent years have witnessed increasing numbers of artificial intelligence(AI)based applications and devices being tested and approved for medical care.Diabetes is arguably the most common chronic disorder worldwide an...Recent years have witnessed increasing numbers of artificial intelligence(AI)based applications and devices being tested and approved for medical care.Diabetes is arguably the most common chronic disorder worldwide and AI is now being used for making an early diagnosis,to predict and diagnose early complications,increase adherence to therapy,and even motivate patients to manage diabetes and maintain glycemic control.However,these AI applications have largely been tested in non-critically ill patients and aid in managing chronic problems.Intensive care units(ICUs)have a dynamic environment generating huge data,which AI can extract and organize simultaneously,thus analysing many variables for diagnostic and/or therapeutic purposes in order to predict outcomes of interest.Even non-diabetic ICU patients are at risk of developing hypo or hyperglycemia,complicating their ICU course and affecting outcomes.In addition,to maintain glycemic control frequent blood sampling and insulin dose adjustments are required,increasing nursing workload and chances of error.AI has the potential to improve glycemic control while reducing the nursing workload and errors.Continuous glucose monitoring(CGM)devices,which are Food and Drug Administration(FDA)approved for use in non-critically ill patients,are now being recommended for use in specific ICU populations with increased accuracy.AI based devices including artificial pancreas and CGM regulated insulin infusion system have shown promise as comprehensive glycemic control solutions in critically ill patients.Even though many of these AI applications have shown potential,these devices need to be tested in larger number of ICU patients,have wider availability,show favorable cost-benefit ratio and be amenable for easy integration into the existing healthcare systems,before they become acceptable to ICU physicians for routine use.展开更多
文摘Artificial intelligence(AI)is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies,such as graphic processing unit,Internet of Things,cloud computing,and the blockchain,in the new generation of big data and Industry 4.0.In this paper,we construct an extensive survey over the period 1961-2018 of AI and deep learning.The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI,from underlying mechanisms to practical applications,from fundamental algorithms to industrial achievements,from current status to future trends.Although there exist many issues toward AI,it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields.
基金This work was supported in part by the National Natural Science Foundation of China(No.61573094 and No.61433004)the Fundamental Research Funds for the Central Universities(N170405002).
文摘The development of diversified energy structures,distributed energy scheduling models and active participation ability of users,leads to a rapid movement toward energy system in which different energy carriers and systems interact together in a synergistic way.This energy development will face many challenges with the requirements of big data processing capability,professional skill,distributed collaboration and realtime monitoring for the energy system that demands an intelligent and flexible tool to realize the smart energy.Artificial intelligence(AI)technology has become a focus because of its better performance.This paper proposed a classification method that incorporates the intelligence of an independent energy unit(IEU)and the intelligence among interconnected energy units(IEUS)to review the development of AI technology in energy systems.The dominant structures of IEU can be considered from three aspects including perception,decision and implementation to study the optimal strategy for AI methods utilized in IEU.And considering the interaction relationship of IEUS,the AI applied for it can be described by the coordinated relationship and adversarial relationship problems to achieve consensus.By discussing the AI technologies and the potentials of AI in the energy system,some suggestions are presented to improve intelligent technologies for sustainable energy systems in the future.
文摘Sepsis remains a major challenge internationally for healthcare systems.Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management.In sepsis,mortality increases with every hour left untreated.Artificial intelligence(AI)is transforming worldwide healthcare delivery at present.This review has outlined how AI can augment strategies to address this global disease burden.AI and machine learning(ML)algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods.Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs.This gives providers additional time to plan and execute treatment plans,thereby avoiding increasing complications associated with delayed diagnosis of sepsis.The potential for cost savings with AI implementation is also discussed,including improving workflow efficiencies,reducing administrative costs,and improving healthcare outcomes.Despite these advantages,clinicians have been slow to adopt AI into clinical practice.Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use.Furthermore,the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies.Finally,we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology。
文摘Gastrointestinal(GI)complications frequently necessitate intensive care unit(ICU)admission.Additionally,critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions.However,these patients form a vulnerable group,who are at risk for developing side effects and complications.Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients.Artificial intelligence(AI)is a rapidly evolving technology with several potential applications in healthcare settings.ICUs produce a large amount of data,which may be employed for creation of AI algorithms,and provide a lucrative opportunity for application of AI.However,the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.
文摘Several studies exist in the literature regarding the exploitation of artificial intelligence in intensive care.However,an important gap between clinical research and daily clinical practice still exists that can only be bridged by robust validation studies carried out by multidisciplinary teams.
文摘Recent years have witnessed increasing numbers of artificial intelligence(AI)based applications and devices being tested and approved for medical care.Diabetes is arguably the most common chronic disorder worldwide and AI is now being used for making an early diagnosis,to predict and diagnose early complications,increase adherence to therapy,and even motivate patients to manage diabetes and maintain glycemic control.However,these AI applications have largely been tested in non-critically ill patients and aid in managing chronic problems.Intensive care units(ICUs)have a dynamic environment generating huge data,which AI can extract and organize simultaneously,thus analysing many variables for diagnostic and/or therapeutic purposes in order to predict outcomes of interest.Even non-diabetic ICU patients are at risk of developing hypo or hyperglycemia,complicating their ICU course and affecting outcomes.In addition,to maintain glycemic control frequent blood sampling and insulin dose adjustments are required,increasing nursing workload and chances of error.AI has the potential to improve glycemic control while reducing the nursing workload and errors.Continuous glucose monitoring(CGM)devices,which are Food and Drug Administration(FDA)approved for use in non-critically ill patients,are now being recommended for use in specific ICU populations with increased accuracy.AI based devices including artificial pancreas and CGM regulated insulin infusion system have shown promise as comprehensive glycemic control solutions in critically ill patients.Even though many of these AI applications have shown potential,these devices need to be tested in larger number of ICU patients,have wider availability,show favorable cost-benefit ratio and be amenable for easy integration into the existing healthcare systems,before they become acceptable to ICU physicians for routine use.
