Fig 3 - uploaded by Deepali Bajaj
Content may be subject to copyright.
Context in source publication
Context 1
... database of responses are created by using information from the National Health Portal [23] to ensure quality responses. For our use-case, our conversational system flow of dialogue has been intended to handle the following use-cases (as shown in figure 3): 1) Providing information about the majority of prevalent diseases in India along with their possible symptoms and applicable preventive measures. 2) Home remedies and quick remedies for common illnesses. ...
Similar publications
Introduction
Mental health issues have been on the rise among children and adolescents, and digital parenting programs have shown promising outcomes. However, there is limited research on the potential efficacy of utilizing chatbots to promote parental skills. This study aimed to understand whether parents learn from a parenting chatbot micro inter...
Question answering (QA) is a high-quality research setback in NLP (Natural Language Processing). Regardless of being one of the oldest research areas, QA has an appliance in an extensive range of tasks, for example reclaiming information as well as extracting entities. In recent times, QA has also been utilized to build up dialog systems and chatbo...
AI-based chatbots are an emerging technology disrupting the tourism industry. Although chatbots have received increasing attention, there is little evidence of their impact on tourists' decisions to visit a destination. This study evaluates the key attributes of chatbots and their effects on user satisfaction and visit intention. We use structural...
Citations
... Multimodal capabilities, surpassing text-based communication, offer advantages in information acquiring, processing, and presentation, thus enhancing user-friendliness (Abdulrahman and Richards, 2021;Scholten et al., 2019). By eliminating barriers with natural language interactions, vulnerable groups like the elderly or impaired individuals who may struggle with long, complex text inputs are enabled in using the CA, hence overall accessibility is improved (Bharti et al., 2020;Sharma et al., 2022). Voice input is particularly essential in healthcare, enhancing the user experience for impaired patients who might have difficulties typing on small smartphone keyboards (Baldauf et al., 2018). ...
... Effective communication between the CA and the user relies on adapting language to the user's proficiency and knowledge (Bharti et al., 2020;Sokolaj et al., 2023). For instance, technical terms should be presented in universally understandable language or accompanied by explanations (Al-Nazer and Helmy, 2012). ...
... Provide reinforcement capabilities to continuously adapt to the user behavior, learn and improve from feedback, and generate personalized answers. As the use with the CA increases, users benefit from more personalized communication that stems from previous behaviors, their language, and preferences (Bharti et al., 2020;Schlimbach et al., 2023). Special attention is directed towards users in vulnerable situations, ensuring accessibility and inclusivity for individuals with disabilities or those not fluent in the CA's language (Sasseville et al., 2022). ...
As global challenges, such as pandemics, population growth and widespread illnesses, continue to rise, healthcare systems are facing greater strain, resulting in a shortage of resources and increased demands for medical care. Effective communication between healthcare professionals and patients is essential for the provision of good services to prevent confusion and induced anxiety of patients, particularly when medical jargon is employed and not understood. Generative AI (GAI) presents a chance to transform healthcare communication by providing language processing capabilities that enhance patient-centered services. This paper examines how GAI-based conversational agents for explaining medical jargon in healthcare should be designed. We derived eleven design principles from a systematic literature review and evaluated them with nine clinical cardiological scenarios through a prototypical instantiation of an LLM-based conversational agent. The results provide insights for researchers and healthcare providers in form of prescriptive design knowledge to improve patient communication using GAI.
... Many chatbots were developed questions related to the COVID-19 emergencies. Most of them operated by recognizing keywords, patterns, and question-answer pairs and collected data from renowned available resources such as WHO or CDC [9][10][11]. Moreover, some developed bots gathered user information, such as medical history and demographics, to better assess their infection severity [12,13]. ...
In this paper, we introduce a modern standard Arabic (MSA) conversational AI in the field of first aid to assist anyone facing an emergency case. The proposed chatbot -based model not only provide adequate medical assistance, but also engage in empathetic interactions with users. Due to the lack of a reliable dataset that fits our purpose, we collect and construct a dataset of 374 records covering 55 first aid topic. Our chatbot architecture relies fine tuning pretrained language models in its intent classification and employs textual similarity to match user queries to records from the dataset. We evaluate the proposed model based using both a quantitative approach, through performance metrics of different modules, and a qualitative approach using a questionnaire.
... Another approach used around 5500 chat consultations to detect disclosure of suicidal ideation, using a deep-learning-based model incorporating external domain knowledge 28 . Other work used NLP techniques for the development of chatbots [29][30][31] . ...
Chat-based counseling hotlines emerged as a promising low-threshold intervention for youth mental health. However, despite the resulting availability of large text corpora, little work has investigated Natural Language Processing (NLP) applications within this setting. Therefore, this preregistered approach (OSF: XA4PN) utilizes a sample of approximately 19,000 children and young adults that received a chat consultation from a 24/7 crisis service in Germany. Around 800,000 messages were used to predict whether chatters would contact the service again, as this would allow the provision of or redirection to additional treatment. We trained an XGBoost Classifier on the words of the anonymized conversations, using repeated cross-validation and bayesian optimization for hyperparameter search. The best model was able to achieve an AUROC score of 0.68 ( p < 0.01) on the previously unseen 3942 newest consultations. A shapely-based explainability approach revealed that words indicating younger age or female gender and terms related to self-harm and suicidal thoughts were associated with a higher chance of recontacting. We conclude that NLP-based predictions of recurrent contact are a promising path toward personalized care at chat hotlines.
... Navigating the vast landscape of the internet can be a challenging task for users, often resulting in frustration and diminished satisfaction. [3] The paper, "Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele Health after COVID-19," introduces a novel approach to providing primary healthcare education and advice to chronic patients in India through a multilingual conversational bot named "Aapka Chikitsak." The chatbot leverages natural language processing (NLP) and voice user interface (VUI) to offer information, preventive measures, home remedies, and interactive counseling sessions, with the aim of increasing healthcare access, especially in rural areas, and addressing challenges posed by the COVID-19 pandemics. ...
Official websites often suffer from navigation challenges that delay users from finding crucial information
effectively. This project proposes the implementation of innovative technologies to transform the navigation
experience. By utilizing AI-driven assistance, the project aims to deliver a user-centric, intuitive browsing
experience. This initiative seeks to enhance official websites' accessibility and user satisfaction, ultimately
improving their effectiveness in serving the public.
... Since the COVID-19 outbreak, chatbots have been used to provide remote assessments to triage potential patients [41,42], expand access to health care education, and try to ease supply and demand challenges for human health care providers [43]. Challenges to wider adoption include the need for more humanlike conversations and social and ethical considerations [13,34,44,45]. ...
Background
The COVID-19 pandemic influenced many to consider methods to reduce human contact and ease the burden placed on health care workers. Conversational agents or chatbots are a set of technologies that may aid with these challenges. They may provide useful interactions for users, potentially reducing the health care worker burden while increasing user satisfaction. Research aims to understand these potential impacts of chatbots and conversational recommender systems and their associated design features.
Objective
The objective of this study was to evaluate user perceptions of the helpfulness of an artificial intelligence chatbot that was offered free to the public in response to COVID-19. The chatbot engaged patients and provided educational information and the opportunity to report symptoms, understand personal risks, and receive referrals for care.
Methods
A cross-sectional study design was used to analyze 82,222 chats collected from patients in South Carolina seeking services from the Prisma Health system. Chi-square tests and multinomial logistic regression analyses were conducted to assess the relationship between reported risk factors and perceived chat helpfulness using chats started between April 24, 2020, and April 21, 2022.
Results
A total of 82,222 chat series were started with at least one question or response on record; 53,805 symptom checker questions with at least one COVID-19–related activity series were completed, with 5191 individuals clicking further to receive a virtual video visit and 2215 clicking further to make an appointment with a local physician. Patients who were aged >65 years (P<.001), reported comorbidities (P<.001), had been in contact with a person with COVID-19 in the last 14 days (P<.001), and responded to symptom checker questions that placed them at a higher risk of COVID-19 (P<.001) were 1.8 times more likely to report the chat as helpful than those who reported lower risk factors. Users who engaged with the chatbot to conduct a series of activities were more likely to find the chat helpful (P<.001), including seeking COVID-19 information (3.97-4.07 times), in-person appointments (2.46-1.99 times), telehealth appointments with a nearby provider (2.48-1.9 times), or vaccination (2.9-3.85 times) compared with those who did not perform any of these activities.
Conclusions
Chatbots that are designed to target high-risk user groups and provide relevant actionable items may be perceived as a helpful approach to early contact with the health system for assessing communicable disease symptoms and follow-up care options at home before virtual or in-person contact with health care providers. The results identified and validated significant design factors for conversational recommender systems, including triangulating a high-risk target user population and providing relevant actionable items for users to choose from as part of user engagement.
... Several approaches have been explored to exploit AI's capabilities for various healthcare applications, including disease diagnosis, predictive modelling, patient monitoring, and personalized medicine [6]. In addition, conversational AI models like ChatGPT can be trained to interact with patients, understand the symptoms and medical history, and provide preliminary guidance for further evaluation [7]. ChatGPT has demonstrated in report-generation tasks. ...
... AI's data mining, pattern recognition, and predictive modeling capabilities can be harnessed to support and enhance clinical decision-making [6]. In addition, conversational AI models like ChatGPT can be trained to interact with patients, understand their symptoms and medical history, and provide preliminary guidance for further evaluation [7]. However, the deployment of AI in healthcare has encountered a notable gap: the need for a structured framework to guide the design and utilization of AI systems like ChatGPT for patient interactions and early disease classification [8]. ...
... A voice application was developed by Bharti et al. [4], which uses natural language processing to assist chronic patients and pregnant women with basic healthcare education and counseling. The program is capable of supporting several languages. ...
Conversational artificial intelligence (AI) is a type of artificial intelligence that uses machine learning techniques to understand and respond to user inputs. This paper presents a conversational chat system that uses an attention mechanism to respond to COVID-19 inquiries. The model is based on the Luong Attention Mechanism’s three scoring methodologies the Dot Attention Mechanism, the General Attention Mechanism, and the Concat Attention Mechanism. The results show that the accuracy of the dot attention mechanism is highest and is 87% when the test questions were obtained directly from the database, as determined by an examination of the results, compared to 38% when the attention mechanism is not used. Furthermore, when the questions are asked with natural variations, human verification accuracy is 63% compared to 16% when the attention mechanism is not used. The research suggests that chatbots can be used everywhere due to their accuracy and accessibility around the clock.
... Through the years, telemedicine is increasingly considered as a vital complement to traditional face-to-face medicine. This can be attributed to several advantages it has, including facilitation for access to care people who living in the areas that are in shortage of medical services like rural areas, reducing healthcare cost, etc. [1] The technique of telemedicine has two integral parts thus far. One is search engine, which enables people to obtain the answers of their medical questions by entering some associated words and phrases in the search box. ...
Background: A large collection of dialogues between patients and doctors are needed to be annotated for medical named entities to build intelligence for telemedicine. However, since most patients involved in telemedicine deliver related named entities in an informal and sentence-level multi-word expression way, it is challenging to tag them on the data of telemedicine dialogues. Under such circumstance, this study aims to address this issue.
Methods: On the data of telemedicine dialogues from Haodf, we have developed guidelines and followed two-round procedure to tag six types of named entities, including disease, symptom, time, pharmaceutical, operation, and examination. Moreover, we have experimented four deep-learning models on the dataset to establish a benchmark for named entity recognition.
Results: The distilled dataset contains 2,383 consultations between doctors and patients, 13,411 sentences from doctors, 17,929 from patients. The average characters per consultation is 1,100. There is 63,560 named entities on the whole, and average characters per named entity is 4.33.Moreover, the experiment results suggest that LatticeLSTM performs best on our dataset regarding all scores like accuracy, precision, F1, etc.
Conclusion: Compared with other exiting datasets, the novelties of this dataset are reflected in three facets: First, the intricated tagging of long multi-word expressions for medical named entity has been tackled in this study. Second, it is one of first attempts to mark temporal entities. Third, this dataset is balanced across the six types of labels. We believe that this dataset will play a considerable role in expanding telemedicine AI.
... Zhang et al. (2017) explore medical query intents. Most of the works are done for English and Chinese languages and there is no proper architecture for Indian multilingual scenarios for intent and entity extraction.. C) Health Care in Indian Languages: Some researchers focus on Indian Languages -Hindi Medical Conversation system, MedBot (Bharti et al., 2020), detecting Hindi and English COVID-19 posts (Patwa et al., 2021), Tamil health information (Santosh, 2019), Bengali health-bot (Badlani et al., 2021), Telugu COVID-19 health information (Vishwas et al., 2017). But none of the work aims at Indian health query datasets and model analysis. ...
Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare. To assess the current status of various state-of-the-art language models in healthcare, this paper studies the problem by initially proposing two different Healthcare datasets, Indian Healthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real world Indian hospital query data in English and multiple Indic languages (Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with the query intents as well as entities. Our aim is to detect query intents and extract corresponding entities. We perform extensive experiments on a set of models in various realistic settings and explore two scenarios based on the access to English data only (less costly) and access to target language data (more expensive). We analyze context specific practical relevancy through empirical analysis. The results, expressed in terms of overall F1 score show that our approach is practically useful to identify intents and entities.
