artificial intelligence

摘要： Artificial intelligence(AI)is defined as the digital computer or computer-controlled robot's ability to mimic intelligent conduct and crucial thinking commonly associated with intelligent beings.The application of AI technology and machine learning in medicine have allowed medical practitioners to provide patients with better quality of services;and current advancements have led to a dramatic change in the healthcare system.However,many efficient applications are still in their initial stages,which need further evaluations to improve and develop these applications.Clinicians must recognize and acclimate themselves with the developments in AI technology to improve their delivery of healthcare services;but for this to be possible,a significant revision of medical education is needed to provide future leaders with the required competencies.This article reviews the potential and limitations of AI in healthcare,as well as the current medical application trends including healthcare administration,clinical decision assistance,patient health monitoring,healthcare resource allocation,medical research,and public health policy development.Also,future possibilities for further clinical and scientific practice were also summarized.

摘要： New technologies such as artificial intelligence,the internet of things,big data,and cloud computing have changed the overall society and economy,and the medical field particularly has tried to combine traditional examination methods and new technologies.The most remarkable field in medical research is the technology of predicting high dementia risk group using big data and artificial intelligence.This review introduces:(1)the definition,main concepts,and classification of machine learning and overall distinction of it from traditional statistical analysis models;and(2)the latest studies in mental science to detect dementia and predict high-risk groups in order to help competent researchers who are challenging medical artificial intelligence in the field of psychiatry.As a result of reviewing 4 studies that used machine learning to discriminate high-risk groups of dementia,various machine learning algorithms such as boosting model,artificial neural network,and random forest were used for predicting dementia.The development of machine learning algorithms will change primary care by applying advanced machine learning algorithms to detect high dementia risk groups in the future.

摘要： In the recent years,a dramatic boost of the research is observed at the junction of photonics,machine learning and artifi-cial intelligence.A new methodology can be applied to the description of a variety of photonic systems including optical waveguides,nanoantennas,and metasurfaces.These novel approaches underpin the fundamental principles of light-matter interaction developed for a smart design of intelligent photonic devices.Artificial intelligence and machine learn-ing penetrate rapidly into the fundamental physics of light,and they provide effective tools for the study of the field of metaphotonics driven by optically induced electric and magnetic resonances.Here we overview the evaluation of meta-photonics induced by artificial intelligence and present a summary of the concepts of machine learning with some specif-ic examples developed and demonstrated for metasystems and metasurfaces.

摘要： The process of selecting an artificial intelligence(AI)model to assist clinical diagnosis of a particular pathology and its validation tests is relevant since the values of accuracy,sensitivity and specificity may not reflect the behavior of the method in a real environment.Here,we provide helpful considerations to increase the success of using an AI model in clinical practice.

摘要： With the rapid development of artificial intelligence technology,the way we interact with products and the environment has undergone great changes compared with the past.While the technology of the artificial intelligence era affects our lives and redefines our relationship with machines,it is also changing our aesthetic and perception of art design.The article mainly starts from the perspective of art design interaction mode and experience innovation,and studies the multi-dimensional aesthetic perception and interaction mode of art design in the era of artificial intelligence in human vision,touch and hearing.

摘要： Artificial intelligence(AI)is defined as the theory and development of computer systems able to perform tasks normally requiring human intelligence,such as visual perception,speech recognition,and decision-making.Machine learning and deep learning(DL)are subfields of AI that are able to learn from experience in order to complete tasks.AI and its subfields,in particular DL,have been applied in numerous fields of medicine,especially in the cure of cancer.Computer vision(CV)system has improved diagnostic accuracy both in histopathology analyses and radiology.In surgery,CV has been used to design navigation system and robotic-assisted surgical tools that increased the safety and efficiency of oncological surgery by minimizing human error.By learning the basis of AI,surgeons can take part in this revolution to optimize surgical care of oncologic disease.

摘要： The management of patients with liver metastases from colorectal cancer is still debated.Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario.Adequate prediction of patients’outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach.This has been an unmet need for a long time,but recent studies have opened new perspectives.New morphological biomarkers have been identified.The dynamic evaluation of the metastases across a time interval,with or without chemotherapy,provided a reliable assessment of the tumor biology.Genetics have been explored and,thanks to their strong association with prognosis,have the potential to drive treatment planning.The liver-tumor interface has been identified as one of the main determinants of tumor progression,and its components,in particular the immune infiltrate,are the focus of major research.Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice.Artificial intelligence is a further step forward.The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases,facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.

摘要： Gastric cancer is widespread globally,and disease diagnosis is accompanied by high mortality and morbidity rates.However,prognoses and survivability have improved following implementation of surveillance and screening programs,which have led to earlier diagnoses.Indeed,early diagnosis itself supports increased surgical curability,which is the main treatment goal and guides therapeutic choice.The most recent Japanese guidelines for endoscopic submucosal dissection and endoscopic mucosal resection for early gastric cancer consider the degree of endoscopic curability in relation to the characteristics of the gastric lesions.In clinical practice,the management approach for both prevention and treatment should be similar to that of colon lesions;however,unlike the established practices for colorectal cancer,the diagnostic and therapeutic pathways are not shared nor widespread for gastric cancer.Ultimately,this negatively impacts the opportunity to perform an endoscopic resection with curative intent.

摘要： The development of network and information technology has brought changes to the information environment.The sources of information are becoming more diverse,and intelligence acquisition will be more complicated.The intelligence reflected by different dimensions of scientific and technologi-cal(S&T)data will have their own focuses.It has become inevitable to carry out the multi-dimen-sional research of S&T frontier,which is also a current research hotspot.This paper uses quantita-tive and qualitative research methods to conduct research and analysis of S&T frontier detection from three dimensions including S&T research projects,S&T papers and patents,and proposes related re-search methods and development tools.This work analyzes the S&T frontiers in the field of artificial intelligence and draws conclusions based on the analysis results of real and effective S&T data in three dimensions.

摘要： BACKGROUND:Computed tomography(CT)is a noninvasive imaging approach to assist the early diagnosis of pneumonia.However,coronavirus disease 2019(COVID-19)shares similar imaging features with other types of pneumonia,which makes differential diagnosis problematic.Artificial intelligence(AI)has been proven successful in the medical imaging field,which has helped disease identification.However,whether AI can be used to identify the severity of COVID-19 is still underdetermined.METHODS:Data were extracted from 140 patients with confirmed COVID-19.The severity of COVID-19 patients(severe vs.non-severe)was defined at admission,according to American Thoracic Society(ATS)guidelines for community-acquired pneumonia(CAP).The AI-CT rating system constructed by Hangzhou YITU Healthcare Technology Co.,Ltd.was used as the analysis tool to analyze chest CT images.RESULTS:A total of 117 diagnosed cases were enrolled,with 40 severe cases and 77 non-severe cases.Severe patients had more dyspnea symptoms on admission(12 vs.3),higher acute physiology and chronic health evaluation(APACHE)II(9 vs.4)and sequential organ failure assessment(SOFA)(3 vs.1)scores,as well as higher CT semiquantitative rating scores(4 vs.1)and AI-CT rating scores than non-severe patients(P<0.001).The AI-CT score was more predictive of the severity of COVID-19(AUC=0.929),and ground-glass opacity(GGO)was more predictive of further intubation and mechanical ventilation(AUC=0.836).Furthermore,the CT semiquantitative score was linearly associated with the AI-CT rating system(Adj R2=75.5%,P<0.001).CONCLUSIONS:AI technology could be used to evaluate disease severity in COVID-19 patients.Although it could not be considered an independent factor,there was no doubt that GGOs displayed more predictive value for further mechanical ventilation.

摘要： With computer graphics and hardware technology, the rapid development of three-dimensionalrendering of the graphics hardware and the quality of the game has grown to the point of near-extreme, artificial intelligence has become a decided success of a game development studio, and related the fate of the gamean important factor. Select the correct and suitable artificial intelligence algorithms are extremely important.This paper analyzes the development of Sino-African genetic algorithm independent role in the game designof some applications. Genetic algorithms as a function of self-learning algorithm, the design of non-playercontrolled character will play a more rational application of the desired results, through the role of self-study,To improve the playability of the game, lay the foundation for the success of the game.

摘要： With computer graphics and hardware technology, the rapid development of three-dimensionalrendering of the graphics hardware and the quality of the game has grown to the point of near-extreme, artificial intelligence has become a decided success of a game development studio, and related the fate of the gamean important factor. Select the correct and suitable artificial intelligence algorithms are extremely important.This paper analyzes the development of Sino-African genetic algorithm independent role in the game designof some applications. Genetic algorithms as a function of self-learning algorithm, the design of non-playercontrolled character will play a more rational application of the desired results, through the role of self-study,To improve the playability of the game, lay the foundation for the success of the game.

摘要： With computer graphics and hardware technology, the rapid development of three-dimensionalrendering of the graphics hardware and the quality of the game has grown to the point of near-extreme, artificial intelligence has become a decided success of a game development studio, and related the fate of the gamean important factor. Select the correct and suitable artificial intelligence algorithms are extremely important.This paper analyzes the development of Sino-African genetic algorithm independent role in the game designof some applications. Genetic algorithms as a function of self-learning algorithm, the design of non-playercontrolled character will play a more rational application of the desired results, through the role of self-study,To improve the playability of the game, lay the foundation for the success of the game.

摘要： With computer graphics and hardware technology, the rapid development of three-dimensionalrendering of the graphics hardware and the quality of the game has grown to the point of near-extreme, artificial intelligence has become a decided success of a game development studio, and related the fate of the gamean important factor. Select the correct and suitable artificial intelligence algorithms are extremely important.This paper analyzes the development of Sino-African genetic algorithm independent role in the game designof some applications. Genetic algorithms as a function of self-learning algorithm, the design of non-playercontrolled character will play a more rational application of the desired results, through the role of self-study,To improve the playability of the game, lay the foundation for the success of the game.