Medical Chatbot Research Articles

REQUEST RESPONSE SYSTEM USING CONVOLUTIONAL NEURAL NETWORKIN MEDICAL INDUSTRY

You must take care of your health if you want to live a long and healthy life. Many times, people are unaware of all the possible diagnoses, treatments, or signs of a specific illness. For young people, it is quite difficult to get a doctor's appointment for any health issues and managing phone calls, so users with the problem travel to the hospital in person for a time-consuming test. By offering relevant advice on leading a healthy lifestyle, healthcare/medical chatbots can help with these kinds of issues. The idea is to use neural networks to build a medical chatbot that can provide information, identify diseases, and provide basic disease information as well as when and where to seek medical advice. Natural language processing technology, which enables users to post their concerns about any disease as well as their health, is the foundation of the success of medical chatbots. Without physically being at the clinic or hospital, the user can use a chatbot to ask any personal questions about health care. Through a medical chat-bot, this will increase access to medical information while lowering the cost of healthcare. The programme development strategy calls for customer sentiment analysis.

An artificial intelligence-based chatbot for prostate cancer education: Design and patient evaluation study.

Artificial intelligence (AI) is increasingly used in healthcare. AI-based chatbots can act as automated conversational agents, capable of promoting health and providing education at any time. The objective of this study was to develop and evaluate a user-friendly medical chatbot (prostate cancer communication assistant (PROSCA)) for provisioning patient information about early detection of prostate cancer (PC). The chatbot was developed to provide information on prostate diseases, diagnostic tests for PC detection, stages, and treatment options. Ten men aged 49 to 81 years with suspicion of PC were enrolled in this study. Nine of ten patients used the chatbot during the evaluation period and filled out the questionnaires on usage and usability, perceived benefits, and potential for improvement. The chatbot was straightforward to use, with 78% of users not needing any assistance during usage. In total, 89% of the chatbot users in the study experienced a clear to moderate increase in knowledge about PC through the chatbot. All study participants who tested the chatbot would like to re-use a medical chatbot in the future and support the use of chatbots in the clinical routine. Through the introduction of the chatbot PROSCA, we created and evaluated an innovative evidence-based health information tool in the field of PC, allowing targeted support for doctor-patient communication and offering great potential in raising awareness, patient education, and support. Our study revealed that a medical chatbot in the field of early PC detection is readily accepted and benefits patients as an additional informative tool.

Clinical-chatbot AHP evaluation based on “quality in use” of ISO/IEC 25010

BackgroundConversational agents are currently a valid alternative to humans in first-level interviews with users who need information, even in-depth, about services or products. In application domains such as health care, this technology can become pervasive only if the perceived ”quality in use” is appropriate. How to measure chatbot quality is an open question. The international standard ISO/IEC 25010 proposes a set of characteristics (effectiveness, efficiency, satisfaction, freedom from risk, and context coverage) to be considered when the ”quality in use” of a software system has to be measured. Basic procedureThis study proposes a clinical chatbot comparison method based on quality. The proposed approach is based on Analytic Hierarchy Process methodology (AHP). FindingsOur contribution is twofold. First, we propose a set of measures for each characteristic of ISO/IEC 25010 according to three classes of functionality: providing information, providing prescriptions and process management. Moreover a quantitative method is proposed for making homogeneous the pairwise weights when the AHP is used for the ”quality-in-use” comparison. As a case study, a comparison of two versions of a chatbot was performed. ConclusionsThe results show that the proposed approach provides an effective reference base for performing quality comparisons of medical chatbots compliant with the ISO/IEC 25010 standard.

MEDICAL BOT USING ARTIFICIAL INTELLIGENCE

Nowadays people hardly try to consult a doctor for diagnosing disease, they instead search in internet sources, where the information is scattered and sometimes, required medical advice is not available. So, it is impractical to find the required information. People sometimes ignore the symptoms and later regret their decision. Healthcare is very important to lead a good life. However, it is very difficult to obtain the consultation with the doctor for every health problem. The idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. This will help to reduce healthcare costs and improve accessibility to medical knowledge through medical chatbot. Chatbots are computer programs that use natural language to interact with users. The chatbot stores the data in the database to identify the sentence keywords and to make a query decision and answer the question. Named Entity recognition and cosine similarity is performed to make a reliable reply to the user.

Healthcare Chatbot using Artificial Intelligence

Abstract: Healthcare as one of the vital spheres of human life is badly falling behind technological progress. While e-commerce, hospitality, and the food industry are advanced and augmented with various technological solutions, healthcare still sticks to the traditional way of doing things. Why can you order a pizza in ten seconds but must wait for an appointment with a doctor for days and weeks? How can you make sure that urgent medical help is indeed urgent? Why finding a credible answer to an important question about your health is so annoyingly difficult? I am also deeply concerned about the lack of attention that the healthcare industry receives. And the first and easiest solution I can offer to change it is to implement chatbot technology in healthcare. Healthcare chatbots will never substitute doctors. But they provide plenty of opportunities to facilitate their job or to improve their performance. The medical chat-bots functioning depends on Natural Language Processing which helps users to submit their problems about their health. The user can ask any personal query related to health care through the chatbot without being out physically available to the hospital. By using Google API for voice-text and text-voice conversion query is sent to the chatbot and gets a related answer and displays the answer on the android app. The system’s major concern behind developing this web-based platform is analyzing customers’ sentiments.

HEALTH CARE CHATBOT

Chatbots mimic human speech to make the software more user-friendly or just for amusement purposes. NLP (Natural language Processing) and AI (Artificial intelligence) are new emerging technologies that can be utilized to improve the capacity of chatbots to stimulate a more natural and free-flowing conversation. Chatbots can be used to offer customers support and services as more and more mobile device users switch to more frequent use of texts and messaging Healthcare chatbots provide a customized experience to each user in ways that may be more practical and effective than humans are capable of. By communicating with consumers in a human like manner, a medical chatbot enhances the work of a healthcare professional and aids in enhancing their performance. Medical chatbots are conversational AI-powered tools that facilitate communication between patients, insurance providers, and health care professionals. These bots can be quite useful in providing timely access to pertinent healthcare information to the appropriate parties. We discuss current chatbot research and uses in the fields of medical and health care education, our main areas if concentration includes the use of virtual patients in medical education patient education regarding healthcare issues, and the use of chatbots as course assistants to improve the curriculum for healthcare professionals. Because of their mobility and affordances, chatbots are increasingly being used into the teaching and learning processes in the field of healthcare education. Clients of this application have discussions with the wellbeing chatbot likewise to how they would have discussions with different clients. Through a series of questions, the health chatbot helps people identify their symptoms in order to assist them decide whether or not to see a doctor. People who are unsure of whether their symptoms are temporary or call for further testing from a doctor may find this.

Disease Prediction and Diagnosis Using Machine Learning

Abstract: With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, differentregions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. Healthcare is very important to lead a good life. However, it is very difficult to obtain the consultation with the doctor for every health problem. The idea is to create a medical Chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. This will help to reduce healthcare costs and improve accessibility to medical knowledge through medical Chatbot. The chatbots are computer programs that use natural language to interact with users. The Chatbot stores the data in the database to identify the sentence keywords and to make a query decision and answer the question

CHATDOC(Medical Assistance)

Abstract: We are now living in a world where visiting hospitals for our regular health checkups are becoming difficult due to the afraid of getting infected by different Viruses(COVID 19). Also the number of patients consulting the doctor becoming more and more because of which the patients have to wait for a long time to get consultation from the doctor. Our idea to avoid these type of problems in the Health care field is to create a AI Medical Chatbot using Natural language processing which is used to understand the input which are usually symptoms of different dieseases provided by the user also with the help of Neural networks and different machine learning algorithms the system predicts the disease also it helps the user in providing the medication. The chatbot is designed to provide medication to mild diseases so that user can reduce to visit hospital for mild dieseases. Chatbots are programs that works on Machine learning, Natural language processing(NLP) which uses NLTK to understand the human language.

Why would you use medical chatbots? interview and survey

BackgroundChatbots, empowered by artificial intelligence, are becoming increasingly popular in many fields and have much potential for application in real life situations. However, little attention has been paid to medical chatbots and most existing evidence focuses on technical issues while behavioral research is still lacking. ObjectiveThis study seeks to examine the key factors that can motivate individuals to use medical chatbots. To that end, we have extended the theory of planned behavior by incorporating pertinent constructs derived after interviews with users. A research model and hypotheses are then proposed and tested. MethodsInterviews were first conducted to collect qualitative data from 20 participants based on purposive sampling. Content analysis was then used to find evidence supporting important constructs for a research model. A survey methodology based on convenience sampling was then used to collect data. Totally, 205 valid responses were gathered and analyzed by using partial least squares structural equation modeling to validate the research model. ResultsHealth consciousness and perceived convenience were found positively associated with individuals’ attitudes towards the use of medical chatbots. Moreover, attitude and subjective norm were found to be significantly and positively related to individuals’ intentions to use medical chatbots. ConclusionsThe proposed model with the extended theory of planned behavior is able to predict individuals’ intention to use medical chatbots well. Hospital managers can formulate strategies to improve individuals’ health consciousness and perceptions of convenience to develop the desired attitudes among individuals, using medical chatbots. Further, strategies to improve patients’ awareness of medical chatbots should also be formulated.

Chatbot for Healthcare

Healthcare is a very important thing for everyone to lead a good life. and it is very difficult to obtain the consultation with the doctor for every health problem.it is difficult to consult a doctor for every small problem The idea is to make chatbot for healthacare using Artificial Intelligence that can diagnose the disease and provide the normal basic details about the disease before consulting a doctor. And This will help to reduce healthcare costs and improve accessibility to medical knowledge through medical chatbot. A chatbot is a Computer Software program that conducts a conversation via auditory or textual methods. A system will contain all the users default quarries and it will be saved in database if any customer asked their querry then it will match with saved data then automatically it will give respond for the customer quarry. This project done by using Artificial Intelligence Natural Language with Python platform Ranking and sentence similarity and this a web application where users have to regiter in the applications before login and after patient has to enter his basic details with there id and there id helps usto rember the details of the patient. Keywords:-Chatbot, Healthcare,Chatbot Assistance Artificial Intelligence, Virtual Assistance.

Using Deep Learning Transformer Networks to Identify Symptoms Associated with COVID-19 on Twitter

This study aims to present a methodology to identify, through Twitter posts, predefined symptoms of COVID-19 aided by Deep Learning techniques, namely Transformers Networks. The proposed approach was evaluated on a public Twitter database in Brazilian Portuguese, using user reports of COVID-19 symptoms. We mine the Twitter database, extract phrases with symptoms, compare distributions, and build a database to construct high accuracy Deep Learning networks, which can be used to identify symptoms. We use a crossvalidation procedure to evaluate the result’s performance. Additionally, we interpret the results using a Local Interpretable Model-Agnostic Explanations (LIME) algorithm. We identified 907 tweets containing one or more of the 14 previously chosen COVID-19 symptoms. The most frequently reported symptoms were a cough (392), headache (154), runny nose (143), fever (124), nausea (106), and diarrhea (105) amongst users who reported at least one symptom. The BERT architecture identified all 14 symptoms reported in Twitter phrases in Portuguese, resulting in identifying each symptom with over 97% accuracy and over 0.95 of AUC-ROC at the test dataset. This project is a step towards a complementary tool to identify symptoms in future automated clinical settings, e.g., medical chatbots, to support faster clinical assessment in Portuguese.

AI based Meddesk Chatbot for Healthcare

-With increasing cases of covid-19 and seasonal diseases it is found that pre-booking bed at hospital is difficult. And this leads to improper treatment on time. This system will come up with a medical chatbot. Chatbots are computer programs that communicate with users using NLP technique. The chatbot stores the information into a database to identify and process the keywords from the sentences. This AI based medical chatbot can take decisions as per the request of the user by using its database. Through chatbot one can speak with a text interface and get answers through AI. In the system, there will be focus on building a chatbot which can easily recommend nearby hospitals and can also provide bed booking facility, nearby vaccination centers and doctor’s appointments. The functioning of medical chat-bots depends on Natural language processing. Many experts and doctors across the globe believe that getting a COVID-19 vaccine helps us keep safe from getting serious illness even if you get affected by COVID positive patient. After getting vaccinated, it will protect other people around, and can save lives as well as reduce risk.

A medical Chatbot using machine learning and natural language understanding

A medical Chatbot using machine learning and natural language understanding

FlauBERT vs. CamemBERT: Understanding patient's answers by a French medical chatbot

In a number of circumstances, obtaining health-related information from a patient is time-consuming, whereas a chatbot interacting efficiently with that patient might help saving health care professional time and better assisting the patient. Making a chatbot understand patients' answers uses Natural Language Understanding (NLU) technology that relies on ‘intent’ and ‘slot’ predictions. Over the last few years, language models (such as BERT) pre-trained on huge amounts of data achieved state-of-the-art intent and slot predictions by connecting a neural network architecture (e.g., linear, recurrent, long short-term memory, or bidirectional long short-term memory) and fine-tuning all language model and neural network parameters end-to-end. Currently, two language models are specialized in French language: FlauBERT and CamemBERT. This study was designed to find out which combination of language model and neural network architecture was the best for intent and slot prediction by a chatbot from a French corpus of clinical cases. The comparisons showed that FlauBERT performed better than CamemBERT whatever the network architecture used and that complex architectures did not significantly improve performance vs. simple ones whatever the language model. Thus, in the medical field, the results support recommending FlauBERT with a simple linear network architecture.

Smart Hospital Chatbot-Virtual Doctor Consultation and Appointment

Healthcare services face a huge challenge of supply-and-demand which you can fix when we create a digital platform. In the traditional healthcare system, patients would go to the hospitals, and they always have to wait for appointments and medical reports. Also, conversation between doctors and patients about their health status and daily lives is not fully remembered by the doctor as well as patients. So, to improve the doctor-patient interaction, we will design and implement a machine learning framework that contains a chatbot, voice transcription and other functionalities. The proposed Medical Chatbot can interact with the users, giving them a realistic experience of chatting with a medical Professional. Our motive is to show that the proposed medical chatbot could be a better alternative to many already existing chatbots in the domain of medicine. An Al enabled conversational UX can deliver personalized experiences to your patients for identifying the illness, scheduling doctor appointments, notifying caregivers about symptoms, monitoring the health status, updating the homecare assistant from time-to time and more. Also, the proposed system converts the voice to text first and then generates prescriptions automatically by extracting the keywords and provides the prescription in the desired format automatically. This system can also generate prescriptions efficiently with just the audio file of the conversation between the doctor and the patient through a phone call.

An AI-Based Medical Chatbot Model for Infectious Disease Prediction

The purpose of this paper is to show concisely how we can promote chatbots in the medical sector and cure infectious diseases. We can create awareness through the users and the users can get proper medical solutions to prevent disease. We created a preliminary training model and a study report to improve human interaction in databases in 2021. Through natural language processing, we describe the human behaviors and characteristics of the chatbot. In this paper, we propose an AI Chatbot interaction and prediction model using a deep feedforward multilayer perceptron. Our analysis discovered a gap in knowledge about theoretical guidelines and practical recommendations for creating AI chatbots for lifestyle improvement programs. A brief comparison of our proposed model concerning the time complexity and accuracy of testing is also discussed in this paper. In our work, the loss is a minimum of 0.1232 and the highest accuracy is 94.32%. This study describes the functionalities and possible applications of medical chatbots and explores the accompanying challenges posed by the use of these emerging technologies during such health crises mainly posed by pandemics. We believe that our findings will help researchers get a better understanding of the layout and applications of these revolutionary technologies, which will be required for continuous improvement in medical chatbot functionality and will be useful in avoiding COVID-19.

Medical chatbot using AI and NLP

The use of chatbots has grown rapidly across industries, including marketing, assistive systems, education, healthcare, cultural heritage, and entertainment. This paper discusses the incentives for using chatbots and explains how useful chatbots are in various contexts. As intelligent software and hardware, also known as intelligent agents, are developed and analyzed, Artificial Intelligence (AI) is becoming more and more integrated into daily lives. From manual labor to complex procedures, intelligent agents are capable of performing a wide range of tasks. One of the simplest and most common forms of intelligent human-computer interaction is the chatbot, which is a classic example of an artificial intelligence Human-Computer Interaction (HCI) system. A chatbot is described as "a computer program designed to simulate interaction with human users, particularly over the Internet." In addition to chatbots, it also called smart bots, interactive agents, digital assistants, and intelligent conversational objects. In the midst of the COVID-19 pandemic, going to the doctor is no longer an indulgence. A chatbot is a Natural Language Processing (NLP) based chatbot to help with basic medical questions. Only the best knowledge of a chatbot can be used to answer medical questions.

Intelligent Medical Chatbot System For Women’s Healthcare

Even today talking about women health is big taboo in India. The society is unbiased about women especially about their health issues hence they face disparity in the society. Women feel hesitant to talk about or speak about their health issues or problems openly. Thus, the goal of this chatbot is to help women to find information and remedial solutions about their health. Query is processed by the bot and response will be displayed on web application. This chatbot will provide helpful information instantly. It will also provide prediction about the disease that the women might be suffering.So this bot will help to make Right decision and give right advice to women on 24/7 basis. It will act as an helping hand for working women to keep check on their health in their busy routine. At the same time this will also help women in rural areas who are apprehensive to talk about their health issues publicly.

A REVIEW ON SMART HEALTHCARE USING MEDICAL CHATBOTS

In the current situation people’s are getting more concerned about well being and good health of themselves and their families. During this pandemic situation of COVID-19 the peoples are scared of visiting hospitals and lack proper medication. In recent years many technologies have been evolved in the medical field one of which is Medical Chatbot. An automatized medical chatbot is a system with human interaction using natural language diagnosis to provide medical aid. Use of NLP, NLU, AI, ML concepts are being done in development of chatbots. A person can keep an eye on his health using these chatbots. The following paper is a review over the technologies and applications of various proposed chatbots in recent years.

Medical Chatbot (Medibot)

In recent years, medical services are getting more open to a more extensive gathering of individuals with the help of innovation. The ideas of computerized reasoning, machine learning and neural organizations have given generous help with the field of medical services. In the present quick moving world, individuals will in general disregard their wellbeing which may result in a basic issue. Such an issue can be kept away from by utilizing the symptom-driven chatbot. Our project focuses on giving the clients accurate and precise prediction of the disease based on their symptom,The disease envisaging chatbot is created utilizing a Decision tree. For the forecast of sicknesses, we have utilized two arrangement calculations specifically, Decision tree and KNN (K-Nearest Neighbours). The presentation of these strategies are analyzed and dependent on their accuracy, the best model is chosen. According to our project, the accuracy of the Decision Tree and KNN are 95% and 92.5% respectively. Our medical chatbot also provides precautions and measures for the envisaged disease. Our medical chatbot also has a text-to-speech feature.,for providing a human element to the conversation, rather than it being an automated conversation, which may feel mundane to the user.