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Health Chatbot: Design, Implementation, Acceptance and Usage Motivation
... One critical importance of language models in medical chatbot development is their ability to understand and interpret user queries, allowing the chatbot to accurately extract relevant information from user inputs. They employ techniques such as named entity recognition and intent classification to identify medical terms, symptoms, or specific requests to enable effective communication between users and chatbots [22,23]. We also considered the need for the proposed chatbot to be effective and respond in a timely manner, and hence, we focused on pre-trained models that were relatively small in size. ...
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.
The healthcare system has had a difficult time keeping up with the demands of this age. People are experiencing many medical problems as a result of the rapid population growth, which has made it difficult for the medical system to handle and treat. To solve the problem, we employ technology for the health care revolution, which enables us to receive prompt and precise assistance. In this project, we are developing an AI chatbot with the use of artificial intelligence. As a virtual assistant, an AI chatbot assists us in giving accurate information about the problems, prescribes if the problem is minor, and directs users to see a doctor if the situation is serious.We use natural language processing (NLP) technologies in this chatbot to make the conversation sound human. The conversations are often upgraded.Additionally, it has unique features like multilingual support and the ability to schedule doctor appointments for convenient times. Additionally, it looks up the closest dates for the doctor's appointment and assists users in taking their medications on time. To make our model run effectively, we have employed techniques like RAG, Streamlit, LLM, and FAISS in addition to NLP.To organize and analyze PDF files, we have also introduced data intake tools such as PyPDF2.According to the HIPAA Act, which states that maintaining the use of protected data is the most crucial function, the healthcare system must guarantee that data privacy and security are among the most crucial elements. AI technology can be used to develop a chatbot that will transform the healthcare system and enable us to efficiently receive the right therapy at the right moment.
Generative chatbots based on artificial intelligence technology have become an essential channel for people to obtain health information. They provide not only comprehensive health information but also real-time virtual companionship. However, the health information provided by AI may not be completely accurate. Employing a 3 × 2 × 2 experimental design, the research examines the effects of interaction types with AI-generated content (AIGC), specifically under virtual companionship and knowledge acquisition scenarios, on the willingness to share health-related rumors. In addition, it explores the impact of the nature of the rumors (fear vs hope) and the role of altruistic tendencies in this context. The results show that people are more willing to share rumors in a knowledge acquisition situation. Fear-type rumors can stimulate people’s willingness to share more than hope-type rumors. Altruism plays a moderating role, increasing the willingness to share health rumors in the scenario of virtual companionship, while decreasing the willingness to share health rumors in the scenario of knowledge acquisition. These findings support Kelley’s three-dimensional attribution theory and negativity bias theory, and extend these results to the field of human–computer interaction. The results of this study help to understand the rumor spreading mechanism in the context of human–computer interaction and provide theoretical support for the improvement of health chatbots.
Background
Patient reliance on internet resources for clinical information has steadily increased. The recent widespread accessibility of artificial intelligence (AI) tools like ChatGPT has increased patient reliance on these resources while also raising concerns about the accuracy, reliability, and appropriateness of the information they provide. Previous studies have evaluated ChatGPT and found it could accurately respond to questions on common surgeries, such as total hip arthroplasty, but is untested for uncommon procedures like total ankle arthroplasty (TAA). This study evaluates ChatGPT-4’s performance in answering patient questions on TAA and further explores the opportunity for physician involvement in guiding the implementation of this technology.
Methods
Twelve commonly asked patient questions regarding TAA were collated from established sources and posed to ChatGPT-4 without additional input. Four fellowship-trained surgeons independently rated the responses using a 1-4 scale, assessing accuracy and need for clarification. Interrater reliability, divergence, and trends in response content were analyzed to evaluate consistency across responses.
Results
The mean score across all responses was 1.8, indicating an overall satisfactory performance by ChatGPT-4. Ratings were consistently good on factual questions, such as infection risk and success rates, whereas questions requiring nuanced information, such as postoperative protocols and prognosis, received poorer ratings. Significant variability was observed among surgeons’ ratings and between questions, reflecting differences in interpretation and expectations.
Conclusion
ChatGPT-4 demonstrates its potential to reliably provide discrete information for uncommon procedures such as TAA, but it lacks the capability to effectively respond to questions requiring patient- or surgeon-specific insight. This limitation, paired with the growing reliance on AI, highlights the need for AI tools tailored to specific clinical practices to enhance accuracy and relevance in patient education.
Objetivo: Este estudo objetivou avaliar o desempenho de chatbots de inteligência artificial na identificação de problemas relacionados à amamentação. Método: o estudo avaliou o OpenAI ChatGPT3.5, Microsoft Copilot, Google Gemini e o Lhia na identificação de problemas da amamentação. O chatbot Lhia está em desenvolvimento pelo nosso time de pesquisadores. Através do consenso entre profissionais de saúde especialistas em amamentação, foi criado um conjunto de dados de relatos de queixa clínica principal anotada em prontuários de atendimento do Hospital Universitário da Universidade Federal do Maranhão para os testes com três abordagens de comandos do tipo zero-shot. Resultados: o melhor desempenho foi com ChatGPT-3.5, que apresentou acurácia variando de 79% a 93%, fallback de 0% a 7% e F1-score de 75% a 100%. Conclusão: chatbots de inteligência artificial podem ser uma ferramenta promissora para auxiliar mães e profissionais de saúde na detecção precoce de problemas na amamentação.
The popularity of ChatGPT has raised questions surrounding AI’s potential for health use cases. Since the release of ChatGPT in 2022, social media users have shared their prompts and ChatGPT responses on different topics such as health. Despite editorials and opinion articles discussing the potential uses of ChatGPT, there is a lack of a systematic approach to identify and analyze the use cases of ChatGPT in health. This study establishes a framework for gathering and identifying tweets (i.e., posts on social media site “X”, formerly known as Twitter) that discuss health use cases of ChatGPT, integrating topic modeling with constructivist grounded theory (CGT) to organize these topics into common categories. Using this framework, nine topics were identified, which were further grouped into four categories: (1) Clinical Workflow, (2) Wellness, (3), Diseases, and (4) Gender Identity. The Clinical Workflow category was the most popular category, and included four topics: (1) Seeking Advice, (2) Clinical Documentation, (3) Medical Diagnosis, and (4) Medical Treatment. Among the identified topics, “Diet and Workout Plans” was the most popular topic. This research highlights the potential of social media to identify the health use cases and potential health applications of an AI-based chatbot such as ChatGPT. The identified topics and categories can be beneficial for researchers, professionals, companies, and policymakers working on health use cases of AI chatbots.
According to WHO estimates, there is a growing population of over 1 billion people aged above 60 years of age. There has been an increasing shortage of caregivers for the aging populations. This has opened a market of USD 7 billion dollars in senior care alone. In a struggle to care for our elderly and capitalize from this industry in efficiency, this paper presents an empathetic robot that can make day-to-day activities hassle free. It is a voice assistant that can detect the emotion of the speaker and reply with contextual awareness to bridge the superstitious gap between the elderly and technology. It achieves this with multi-modal classification of emotion using audio and text. i) Machine learning model using Librosa to engineer features and Support vector classifier (SVC) , ii) BERT based model using transfer learning to categorize text. The robot can also set reminders for appointments and medicine intake to help with forgetfulness. The robot is customizable by a caregiver or loved one through the web application where details of any messages, reminders, descriptions of prescriptions could be entered. The robot comes with a pill dispenser that can rotate and dispense pills at the correct time and also notify the senior through the speaker. A raspberry pi is used to convert the speech to text and vice versa. The design of this robot paves the path to providing a realistic, care-giving experience to the elderly.
The entry into a hyper-connected society increases the generalization of communication using SNS. Therefore, research to analyze big data accumulated in SNS and extract meaningful information is being conducted in various fields. In particular, with the recent development of Deep Learning, the performance is rapidly improving by applying it to the field of Natural Language Processing, which is a language understanding technology to obtain accurate contextual information. In this paper, when a chatbot system is applied to the healthcare domain for counseling about diseases, the performance of NLP integrated with machine learning for the accurate classification of medical subjects from text-based health counseling data becomes important. Among the various algorithms, the performance of Bidirectional Encoder Representations from Transformers was compared with other algorithms of CNN, RNN, LSTM, and GRU. For this purpose, the health counseling data of Naver Q&A service were crawled as a dataset. KoBERT was used to classify medical subjects according to symptoms and the accuracy of classification results was measured. The simulation results show that KoBERT model performed high performance by more than 5% and close to 18% as large as the smallest.
Today, Artificial Intelligence is already part of our environment and is used in various social, economic, and educational fields. Therefore, incorporating Artificial Intelligence technology in any field is an emerging need because it generates new opportunities to offer better products and services. However, to stay ahead, it is necessary to implement the right technology to change the competitive landscape in organizations and educational institutions. This work proposes the incorporation of ChatBots as a new proposal to perform virtual academic advisories within the Institutional Tutoring Program of the Universidad Autonoma de Sinaloa to strengthen the Peer Advisors program. In this sense, an instrument in Google form has been designed and validated to know the student’s and professors’ opinions of the Faculty of Computer Science before its development.
This paper presents the experiences of an iterative design and implementation of a chatbot for Sexual and Reproductive health education and counselling - Telenanu in Peru. An adequate sexual and reproductive health education is still a challenge in many low- and middle-income countries, especially for young people. The aim of the chatbot is to bring reliable information to adolescents and youth accessible via smartphone and social media. The design and conversation flow of the chatbot follows guidelines and tools from the Ministry of health in Peru and the World Health Organization. A focus group of college students during testing and an open survey once in production was created and conducted. Result show the usefulness of an iterative design chatbot for SRH counselling in adolescents and youth. The friendliness and availability of the service are strong point according to the results
Background
Tuberculosis (TB) is a highly infectious disease. Negative perceptions and insufficient knowledge have made its eradication difficult. Recently, mobile health care interventions, such as an anti-TB chatbot developed by the research team, have emerged in support of TB eradication programs. However, before the anti-TB chatbot is deployed, it is important to understand the factors that predict its acceptance by the population.
Objective
This study aims to explore the acceptance of an anti-TB chatbot that provides information about the disease and its treatment to people vulnerable to TB in South Korea. Thus, we are investigating the factors that predict technology acceptance through qualitative research based on the interviews of patients with TB and homeless facility personnel. We are then verifying the extended Technology Acceptance Model (TAM) and predicting the factors associated with the acceptance of the chatbot.
Methods
In study 1, we conducted interviews with potential chatbot users to extract the factors that predict user acceptance and constructed a conceptual framework based on the TAM. In total, 16 interviews with patients with TB and one focus group interview with 10 experts on TB were conducted. In study 2, we conducted surveys of potential chatbot users to validate the extended TAM. Survey participants were recruited among late-stage patients in TB facilities and members of web-based communities sharing TB information. A total of 123 responses were collected.
ResultsThe results indicate that perceived ease of use and social influence were significantly predictive of perceived usefulness (P=.04 and P
In the post-genomic era, the elucidation of the relationship between the molecular origins of diseases and their resulting phenotypes is a crucial task for medical research. Here, we use a large-scale biomedical literature database to construct a symptom-based human disease network and investigate the connection between clinical manifestations of diseases and their underlying molecular interactions. We find that the symptom-based similarity of two diseases correlates strongly with the number of shared genetic associations and the extent to which their associated proteins interact. Moreover, the diversity of the clinical manifestations of a disease can be related to the connectivity patterns of the underlying protein interaction network. The comprehensive, high-quality map of disease-symptom relations can further be used as a resource helping to address important questions in the field of systems medicine, for example, the identification of unexpected associations between diseases, disease etiology research or drug design.
BOOK is copyrighted and is NOT available for DOWNLOAD. Fourth edition (new & used copies) available from https://www.amazon.com/Doing-Survey-Research-Quantitative-Methods/dp/1138043397
Intended for people who want to learn how to conduct quantitative studies for a project in an undergraduate course, a graduate-level thesis, or a survey that an employer may want completed. This brief, practical textbook prepares beginners to conduct their own survey research and write up the results, as well as read and interpret other people's research. It combines survey design with data analysis and interpretation.
And it is for those who need to understand and critically interpret survey research found in scholarly journals, reports distributed in the workplace, and social scientific findings presented online in the media, on a blog, or in social media postings.
Say hello to molly, Florence, and Ada—they’re just a few of the helpful, smart algorithm powered chatbots taking their place in health care. Chatbots are computer programs designed to carry on a dialogue with people, assisting them via text messages, applications, or instant messaging. Essentially, instead of having a conversation with a person, the user talks with a bot that’s powered by basic rules or artificial intelligence. Chatbots are already widely used to support, expedite, and improve processes in other industries, such as retail, and now, the technology is gaining traction in health care, where it is helping patients and providers perform myriad tasks.
Welcome to the Build Better Chatbots book. Do you remember the last time you had to call a toll-free number for support or customer service? Do you remember the long wait time on the phone before you could even talk about your issue and then realizing somehow you chose the wrong button option leading you to the wrong department? We have had this experience, and that why we created a chatbot for enterprises to use to help resolve customer questions more easily and in an interface that many people, especially millennials, are getting more accustomed to using: chat. In this book, we will take you through the history of chatbots, including when they were invented and how they became popular. We will also show how to build a chatbot for your next project. After completing this book, you will know how to deploy applications with a chat interface on platforms such as Facebook Messenger, Skype, and so on, which automatically respond to user queries without any human intervention.
The vascularization of the heart has been dealt with in numerous papers. This organ is without doubt of especial clinical interest, particularly if the high incidence of ischemic heart diseases is considered. From the morphological point of view, several questions have attracted the interest of researchers. The existence and topology of Thebesian vessels, for example, have been discussed for years [1–5] but general agreement was not achieved amongst the authors. Macroscopic studies of corrosion casts, angiography of coronary vessels, as well as the study of India ink injected specimens served as further methods for demonstrating the variations of coronary vessels, blood supply of the heart conductive system, and pathological changes of myocardial vasculature [6–12]. When the scanning electron microscopy of vascular corrosion casts was applied to the vasculature of the heart, some controversial questions could be cleared up [19, 20].
Chatbots could help with screening patients for COVID-19
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Youper, a chatbot that helps users navigate their emotions, raises $3 milion in seed funding
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