Clinical applications of Artificial Intelligence(AI)for mental health care have experienced a meteoric rise in the past few years.AIenabled chatbot software and applications have been administering significant medical...Clinical applications of Artificial Intelligence(AI)for mental health care have experienced a meteoric rise in the past few years.AIenabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals.Such initiatives,which range from“virtual psychiatrists”to“social robots”in mental health,strive to improve nursing performance and cost management,as well as meeting the mental health needs of vulnerable and underserved populations.Nevertheless,there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings.Furthermore,treatments are frequently developed without clear ethical concerns.While AI-enabled solutions show promise in the realm of mental health,further research is needed to address the ethical and social aspects of these technologies,as well as to establish efficient research and medical practices in this innovative sector.Moreover,the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbotpsychologists development.Taking into account all the problems outlined in this study,we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence.In this systematic review,we put five research questions related to technologies in chatbot development,psychological disorders that can be treated by using chatbots,types of therapies that are enabled in chatbots,machine learning models and techniques in chatbot psychologists,as well as ethical challenges.展开更多
The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate p...The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate problematic social media,and their potential is yet to be fully realized.Emerging large language models(LLMs)are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life.In mitigating problematic social media use,LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users,providing personalized information and resources,monitoring and intervening in problematic social media use,and more.In this process,we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT,leveraging their advantages to better address problematic social media use,while also acknowledging the limitations and potential pitfalls of ChatGPT technology,such as errors,limitations in issue resolution,privacy and security concerns,and potential overreliance.When we leverage the advantages of LLMs to address issues in social media usage,we must adopt a cautious and ethical approach,being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society.展开更多
COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of en...COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.展开更多
<strong>Background:</strong> Chatbots are easy to use and simulate a human conversation through text or voice via smartphones or computers. In the field of health, chatbots can improve patient information,...<strong>Background:</strong> Chatbots are easy to use and simulate a human conversation through text or voice via smartphones or computers. In the field of health, chatbots can improve patient information, monitoring, or treatment adherence. <strong>Method:</strong> The objective of this article is to describe how a chatbot dedicated to disease monitoring and support of patients can interact with them and how data are exploited to be safe. <strong>Results:</strong> Wefight designed a chatbot named Vik to empower patients with cancers or chronic diseases and their relatives via personalized text messages. Natural Language Processing models were used. We built several Vik for each disease. Each Vik has its contents, its own NLP model and interacts its way with the patient. <strong>Conclusion: </strong>Conversational agents may help patients with minor health concerns without seeing a real physician. If the quality of these softwares is not thoroughly assessed, they could be dangerous. If chatbots are effective and safe, they could be prescribed like a drug to improve patient information, monitoring, or treatment adherence.展开更多
Artificial intelligent based dialog systems are getting attention from both business and academic communities.The key parts for such intelligent chatbot systems are domain classification,intent detection,and named ent...Artificial intelligent based dialog systems are getting attention from both business and academic communities.The key parts for such intelligent chatbot systems are domain classification,intent detection,and named entity recognition.Various supervised,unsupervised,and hybrid approaches are used to detect each field.Such intelligent systems,also called natural language understanding systems analyze user requests in sequential order:domain classification,intent,and entity recognition based on the semantic rules of the classified domain.This sequential approach propagates the downstream error;i.e.,if the domain classification model fails to classify the domain,intent and entity recognition fail.Furthermore,training such intelligent system necessitates a large number of user-annotated datasets for each domain.This study proposes a single joint predictive deep neural network framework based on long short-term memory using only a small user-annotated dataset to address these issues.It investigates value added by incorporating unlabeled data from user chatting logs into multi-domain spoken language understanding systems.Systematic experimental analysis of the proposed joint frameworks,along with the semi-supervised multi-domain model,using open-source annotated and unannotated utterances shows robust improvement in the predictive performance of the proposed multi-domain intelligent chatbot over a base joint model and joint model based on adversarial learning.展开更多
People occasionally interact with each other through conversation.In particular,we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language....People occasionally interact with each other through conversation.In particular,we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language.Conversational artificial intelligence is an integral part of all the technologies that allow computers to communicate like humans.For a computer to interact like a human being,it must understand the emotions inherent in the conversation and generate the appropriate responses.However,existing dialogue systems focus only on improving the quality of understanding natural language or generating natural language,excluding emotions.We propose a chatbot based on emotion,which is an essential element in conversation.EP-Bot(an Empathetic PolarisX-based chatbot)is an empathetic chatbot that can better understand a person’s utterance by utilizing PolarisX,an autogrowing knowledge graph.PolarisX extracts new relationship information and expands the knowledge graph automatically.It is helpful for computers to understand a person’s common sense.The proposed EP-Bot extracts knowledge graph embedding using PolarisX and detects emotion and dialog act from the utterance.Then it generates the next utterance using the embeddings.EP-Bot could understand and create a conversation,including the person’s common sense,emotion,and intention.We verify the novelty and accuracy of EP-Bot through the experiments.展开更多
India imposed the largest lockdown in the world in response tofight the spread of the Novel Coronavirus disease(COVID-19)from 19 March till 31 May 2020.The onset of the pandemic left the general public feeling psycho-s...India imposed the largest lockdown in the world in response tofight the spread of the Novel Coronavirus disease(COVID-19)from 19 March till 31 May 2020.The onset of the pandemic left the general public feeling psycho-socially distressed,helpless,and anxious.The researcher developed a Messenger supported Chatbot,based on the broaden and build model,to cater to the healthy general public to promote positivity and mental well-being.31 participants between 22 and 45 years old consensually took a pre-test,Chatbot intervention,and post-test.The Chatbot provided guided activities out of which positive affirmations,meditation,and exercises were mostly used.The qualitative data from the study shows that the majority of the participants strongly feel positivity is within themselves and that the tool provided a self-help approach to be me well,mentally during the lockdown.The intervention helped significantly reducing symptoms of psychosocial distress in six of the individual’s post-chatbot interventions.Participants’impressions of the tool suggest more preponderant opportunities for future research in technology-driven mental health support.展开更多
People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experien...People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope.Chatbots employ Natural Language Understanding(NLU)to infer their responses.There is a need for a chatbot that can learn from inquiries and expand its area of experience with time.This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval.This study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles.We used a COVID-19 information dataset to evaluate the proposed methodology.The pandemic has been accompanied by an“infodemic”of fake news.The chatbot was evaluated by a medical doctor and a public trial of 308 real users.Evaluationswere obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 standard.The proposed COVID-19 chatbot system relieves doctors from answering questions.Chatbots provide an example of the use of technology to handle an infodemic.展开更多
基金This work was supported by the grant“Development of an intellectual system prototype for online-psychological support that can diagnose and improve youth’s psychoemotional state”funded by the Ministry of Education of the Republic of Kazakhstan.Grant No.IRN AP09259140.
文摘Clinical applications of Artificial Intelligence(AI)for mental health care have experienced a meteoric rise in the past few years.AIenabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals.Such initiatives,which range from“virtual psychiatrists”to“social robots”in mental health,strive to improve nursing performance and cost management,as well as meeting the mental health needs of vulnerable and underserved populations.Nevertheless,there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings.Furthermore,treatments are frequently developed without clear ethical concerns.While AI-enabled solutions show promise in the realm of mental health,further research is needed to address the ethical and social aspects of these technologies,as well as to establish efficient research and medical practices in this innovative sector.Moreover,the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbotpsychologists development.Taking into account all the problems outlined in this study,we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence.In this systematic review,we put five research questions related to technologies in chatbot development,psychological disorders that can be treated by using chatbots,types of therapies that are enabled in chatbots,machine learning models and techniques in chatbot psychologists,as well as ethical challenges.
文摘The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate problematic social media,and their potential is yet to be fully realized.Emerging large language models(LLMs)are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life.In mitigating problematic social media use,LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users,providing personalized information and resources,monitoring and intervening in problematic social media use,and more.In this process,we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT,leveraging their advantages to better address problematic social media use,while also acknowledging the limitations and potential pitfalls of ChatGPT technology,such as errors,limitations in issue resolution,privacy and security concerns,and potential overreliance.When we leverage the advantages of LLMs to address issues in social media usage,we must adopt a cautious and ethical approach,being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society.
文摘COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus.The foremost and most prime sector among those affected were schools,colleges,and universities.The education system of entire nations had shifted to online education during this time.Many shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of LMSs.This paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user experience.The AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based chatbots.Session layer enhancements are also required,such as AI-based online proctoring and user authentication using Biometrics.These extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of AI-algorithms.It also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.
文摘<strong>Background:</strong> Chatbots are easy to use and simulate a human conversation through text or voice via smartphones or computers. In the field of health, chatbots can improve patient information, monitoring, or treatment adherence. <strong>Method:</strong> The objective of this article is to describe how a chatbot dedicated to disease monitoring and support of patients can interact with them and how data are exploited to be safe. <strong>Results:</strong> Wefight designed a chatbot named Vik to empower patients with cancers or chronic diseases and their relatives via personalized text messages. Natural Language Processing models were used. We built several Vik for each disease. Each Vik has its contents, its own NLP model and interacts its way with the patient. <strong>Conclusion: </strong>Conversational agents may help patients with minor health concerns without seeing a real physician. If the quality of these softwares is not thoroughly assessed, they could be dangerous. If chatbots are effective and safe, they could be prescribed like a drug to improve patient information, monitoring, or treatment adherence.
基金This research was supported by the BK21 FOUR(Fostering Outstanding Universities for Research)funded by the Ministry of Education(MOE,Korea)and National Research Foundation of Korea(NFR).
文摘Artificial intelligent based dialog systems are getting attention from both business and academic communities.The key parts for such intelligent chatbot systems are domain classification,intent detection,and named entity recognition.Various supervised,unsupervised,and hybrid approaches are used to detect each field.Such intelligent systems,also called natural language understanding systems analyze user requests in sequential order:domain classification,intent,and entity recognition based on the semantic rules of the classified domain.This sequential approach propagates the downstream error;i.e.,if the domain classification model fails to classify the domain,intent and entity recognition fail.Furthermore,training such intelligent system necessitates a large number of user-annotated datasets for each domain.This study proposes a single joint predictive deep neural network framework based on long short-term memory using only a small user-annotated dataset to address these issues.It investigates value added by incorporating unlabeled data from user chatting logs into multi-domain spoken language understanding systems.Systematic experimental analysis of the proposed joint frameworks,along with the semi-supervised multi-domain model,using open-source annotated and unannotated utterances shows robust improvement in the predictive performance of the proposed multi-domain intelligent chatbot over a base joint model and joint model based on adversarial learning.
基金supported by Basic Science Research Program through the NRF(National Research Foundation of Korea)the MSIT(Ministry of Science and ICT),Korea,under the National Program for Excellence in SW supervised by the IITP(Institute for Information&communications Technology Promotion)and the Gachon University research fund of 2019(Nos.NRF2019R1A2C1008412,2015-0-00932,GCU-2019-0773).
文摘People occasionally interact with each other through conversation.In particular,we communicate through dialogue and exchange emotions and information from it.Emotions are essential characteristics of natural language.Conversational artificial intelligence is an integral part of all the technologies that allow computers to communicate like humans.For a computer to interact like a human being,it must understand the emotions inherent in the conversation and generate the appropriate responses.However,existing dialogue systems focus only on improving the quality of understanding natural language or generating natural language,excluding emotions.We propose a chatbot based on emotion,which is an essential element in conversation.EP-Bot(an Empathetic PolarisX-based chatbot)is an empathetic chatbot that can better understand a person’s utterance by utilizing PolarisX,an autogrowing knowledge graph.PolarisX extracts new relationship information and expands the knowledge graph automatically.It is helpful for computers to understand a person’s common sense.The proposed EP-Bot extracts knowledge graph embedding using PolarisX and detects emotion and dialog act from the utterance.Then it generates the next utterance using the embeddings.EP-Bot could understand and create a conversation,including the person’s common sense,emotion,and intention.We verify the novelty and accuracy of EP-Bot through the experiments.
文摘India imposed the largest lockdown in the world in response tofight the spread of the Novel Coronavirus disease(COVID-19)from 19 March till 31 May 2020.The onset of the pandemic left the general public feeling psycho-socially distressed,helpless,and anxious.The researcher developed a Messenger supported Chatbot,based on the broaden and build model,to cater to the healthy general public to promote positivity and mental well-being.31 participants between 22 and 45 years old consensually took a pre-test,Chatbot intervention,and post-test.The Chatbot provided guided activities out of which positive affirmations,meditation,and exercises were mostly used.The qualitative data from the study shows that the majority of the participants strongly feel positivity is within themselves and that the tool provided a self-help approach to be me well,mentally during the lockdown.The intervention helped significantly reducing symptoms of psychosocial distress in six of the individual’s post-chatbot interventions.Participants’impressions of the tool suggest more preponderant opportunities for future research in technology-driven mental health support.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work(Project Number UB-2-1442).
文摘People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased responses.However,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their scope.Chatbots employ Natural Language Understanding(NLU)to infer their responses.There is a need for a chatbot that can learn from inquiries and expand its area of experience with time.This chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast retrieval.This study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative profiles.We used a COVID-19 information dataset to evaluate the proposed methodology.The pandemic has been accompanied by an“infodemic”of fake news.The chatbot was evaluated by a medical doctor and a public trial of 308 real users.Evaluationswere obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 standard.The proposed COVID-19 chatbot system relieves doctors from answering questions.Chatbots provide an example of the use of technology to handle an infodemic.
