Through chatbots one can communicate with text or voice interface and get reply through artificial intelligence. Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as ecommerce customer service, call centres and Internet gaming. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for.

🤖 HealthCare ChatBot Major -1 (4th year - 7th semester) Health Care Chat-Bot is a Healthcare Domain Chatbot to simulate the predictions of a General Physician. ChatBot can be described as software that can chat with people using artificial intelligence. These software are used to perform tasks such as quickly responding to users, informing them, helping to purchase products and providing better service to customers. We have made a healthcare based chatbot. The three main areas where chatbots can be used are diagnostics, patient engagement outside medical facilities, and mental health. In our major we are working on diagnostic. 📃 Brief A chatbot is an artificially intelligent creature which can converse with humans. This could be text-based, or a spoken conversation. In our project we will be using Python as it is currently the most popular language for creating an AI chatbot. In the middle of AI chatbot, architecture is the Natural Language Processing (NLP) layer. This project aims to build an user-friendly healthcare chatbot which facilitates the job of a healthcare provider and helps improve their performance by interacting with users in a human-like way. Through chatbots one can communicate with text or voice interface and get reply through artificial intelligence Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as E-commerce customer service, Call centres, Internet gaming,etc. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for. 📜 Problem Statement During the pandemic, it is more important than ever to get your regular check-ups and to continue to take prescription medications. The healthier you are, the more likely you are to recover quickly from an illness. In this time patients or health care workers within their practice, providers are deferring elective and preventive visits, such as annual physicals. For some, it is not possible to consult online. In this case, to avoid false information, our project can be of help. 📇 Features Register Screen. Sign-in Screen. Generates database for user login system. Offers you a GUI Based Chatbot for patients for diagnosing. [A pragmatic Approach for Diagnosis] Reccomends an appropriate doctor to you for the following symptom. 📜 Modules Used Our program uses a number of python modules to work properly: tkinter os webbrowser numpy pandas matplotlib 📃 Algorithm We have used Decision tree for our health care based chat bot. Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.It usually mimic human thinking ability while making a decision, so it is easy to understand. :suspect: Project Members Anushka Bansal - 500067844 - R164218014 Shreya Sharma - 500068573 - R164218070 Silvi - 500069092 - R164218072 Ishika Agrawal - 500071154 - R164218097

An automatic trained voice-enabled conversational chatbot with images and video support which does not allow you to feel the absence of doctor, hospital staff and management by solving your desired quests.

To provide a free service of interaction with a machine, the objective of “Emotionally Aware Chatbot” is to provide mental healthcare to those who are mentally-ill anywhere and anytime. It raises the question of what role, if any, the chatbot should play in suicide prevention. With this chatbot, we expect to reach as many mentally-ill people as possible by hosting this on web domains of known hospitals or counselors. If it is easy to detect mental health issues at the correct time and provide suitable help, it might save precious lives. If integrated with the hospital systems, this would provide an effective way to automate the work.

Through chatbots one can communicate with text or voice interface and get reply through Artificial intelligence. Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as ecommerce customer service, call centres and Internet gaming. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of Artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for.

Hospital Management System to help Patients and Doctors book appointments via Android/iOS Apps respectively and the administration using a Flask Web App to manage the functions. Chatbot Functionality to help users and act as Customer Support

WhatsApp chatbot with an integrated web application for facilitating online consultations and zero-contact physical hospital visits. Built with Express, Node, MySQL, Sequelize.

Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.

Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for.

A chatbot system targetted towards use in a hospital for administrative tasks.

An open source AI web chatbot that is trained over specific clinic/hospital data to expedite the online patient experience. ClinicChatBot helps to summarize and gather the most relevant medical data that you desire from web content and customer reviews.

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Aurleaf is a medical platform that helps users find nearby Ayurvedic clinics, doctors, and hospitals. It offers AI-generated diet plans, exercise routines, and doctor-approved medical products like blood sugar monitors. Users can also access an AI chatbot for queries and book doctor appointments or lab tests using a pincode-based system.

GraphRag based chatbot built using LangChain and Neo4j, designed for hospital systems. The chatbot retrieves structured and unstructured data about patients, visits, physicians, insurance payers, and hospital locations. This project also explores integrating graph databases and deploying the chatbot using FastAPI & Streamlit

Abstract: Chronic diseases have affected many people globally and causing morbidity, and modality—the impact of chronic disease related to people’s lifestyles and their choices of habits like food, among others. Also, a model that immune and improves patients' lifestyle and habits by giving guidance and follow-up for future symptoms. As seen that there is high demand for specialists/experts in that field. Only a few studies have considered reducing the workload and exhaustion of physicians during the treatment phase. This article aims to provide a preliminary assessment of the Chatbot health system, a conversational agent-assisted health guiding framework to help doctors cope with burnout and provide continuous treatment to their patients. The approach is taken to create the chatbot dialog, and the guiding system is presented in this paper. As a result, talk about the first patient profiling technique for classifying patients based on their cholesterol levels. The machine then makes recommendations to the expert on tasks to include in the patient’s guidance in dialogue. This study makes three significant contributions to disease complications prevention (i.e., preserving a healthier lifestyle): (1) It provides the specialist/expert with a conversational agent to support patients; (2) It reduces experts’/specialists’ workload and safe their time to improves patient care by categorizing patients into groups, each group has its case scenario, that would help to follow up with patients until he visits a clinic/hospital for drugs or meeting doctors face-to-face; and (3) It gives the physician physical/nutrition activity guidelines when creating a general patient activity guiding choices based on his cholesterol level whether its normal, abnormal, or risk.

Indian Hospital Locator Chatbot in Rasa Framework

Chatbots in Tourism Hospitality Industry: The future of chatbot is here; this technology has recently witnessed rapid diffusion in many sectors. Basic versions of chatbots are currently utilized, which usually start conversations with easy automated options for patrons and offer basic services like ordering or booking. However, fully functional chatbots that will be ready to replace customer service personnel will likely become more widespread by 2020, with AI bots powering 85% of all customer service interactions. Chatbots have the potential to assist the tourism industry in many ways – Chatbots in Tourism Hospitality Industry For any industry, accessibility to the company’s offerings is vital to the customer in both the pre-sale and therefore the post-sale process. Now, as more and more people are using instant messaging services like Facebook Messenger and WhatsApp, this simple use is often further enhanced by a company’s offering all of its services where consumers are afore chatting with their friends. Performing common administrative and menial tasks through chatbots, like scheduling appointments, setting reminders, booking tickets, and sharing traffic or weather updates, is very valued. Although there are some potential pitfalls, discussed later, the potential of chatbots in diverse sectors of the tourism industry is gigantic. Hotels, restaurants, hire car services, travel agencies, and tourist information centers can all enjoy this technology. The hotel industry can particularly enjoy the direct application of chatbots. Increasing the share of online bookings impacts sales growth, confirming the value of the hotel chatbot. Expedia took advantage of Facebook’s technology to launch a basic bot to assist travelers book hotels. Marriott Hotels also introduced a chatbot service to supply basic services like booking an area over chat, utilizing the Facebook chatbot interface. Chatbots are often particularly helpful in enriching the prearrival experience, allowing users to book rooms and other amenities, like: Spa Treatments Airport transfers Dinner Reservations Chatbots in the Hotel Industry A bot that interacts with guests in the least stages of the customer journey can gather valuable data, which algorithms and hotel staff alike can then use to supply personalized services. The direct application of chatbots within the restaurant business is often very impactful also. Restaurants and nutriment giants like Burger King, Pizza Hut, and Dominos have followed suit with their proprietary chatbots. Soon placing delivery orders over the phone is going to be obsolete; customers will do that through Facebook, WhatsApp, or other social networking sites. Chatbots will eventually accept payments as well; MasterCard already provides such services through its Masterpass app. Chatbots in the Restaurants Positioning chatbots can decrease costs for both customers and firms. Customers don’t get to call, which reduces their communication expenditures, and corporations will not get to hire customer service representatives or outsource answering services to a call center facility. The advantages aren’t limited to the ordering and delivery processes. Other possible chatbot benefits highlights include allowing customers to perform subsequent tasks without having to download mobile apps: Observe and survey restaurant reviews, menus, prices, and available tables Control restaurant reservations on the go, change, cancel, or re-book tables Search and find restaurants consistent with party size, date, time, preferred cuisine, price, or distance. Chatbots in the Airline Industry – Chatbots in Tourism Hospitality Industry Customer service within the airline industry is one of the primary areas that would enjoy chatbots as a result of the high volume of customer contact through inquiries and bookings. an honest customer service bot could economize by automating tasks and unclogging call centers. It might help consumers find suitable flight options by meeting information like time, date, and other preferences. It could help on the wing booking, saving customers the difficulty of visiting the airline’s website and entering page after page of data. It could give status updates about flights, like information about delays or cancellations. It could also provide digital boarding passes, a service Turkish Airlines has begun to provide; offer baggage information; and gather feedback. it’s reported that its introduction has recorded an enormous surge in online booking. Chatbot Challenges Although AI and chatbots have created excitement within the tourism and hospitality industry, many concerns and problems can affect their adoption. The media’s portrayal of AI as being capable of handling much of the tasks within the tourism and hospitality industry is sometimes overrated. the push toward chatbots is partly thanks to the recognition of several new messaging services. The testing with chatbot adoption involves technical issues, cost, culture, and organization size. one among the foremost significant technical issues in language processing. Chatbots still commonly struggle with lexical and semantic ambiguity. We have study the role of chatbots in several areas of the tourism and hospitality industry. This is often the age of chatbots. As an information-intensive industry, firms that lead in its early adoption are set to experience first-mover advantage, that is, the benefit gained by being the primary to launch a service. The interlinked nature of the tourism industry will subject industry laggards into undue pressures, which can not be favorable to their strategic directions at that point. So, the time to plan is now!

The Apollo Adlux Hospital RAG Chatbot system, Built on the Ollam 3.2 LLM, this chatbot leverages Retrieval-Augmented Generation (RAG) to offer accurate, contextually relevant answers by pulling information from hospital resources and FAQs.

This is a simple chatbot component developed for a hackathon project. It provides basic functionalities like finding hospitals, scheduling appointments, and getting disease information.

Designed and developed a chatbot for hospital management systems using LangChain. The chatbot incorporated RAG with Neo4j graph database for knowledge retrieval, memory chains for context retention, and ChatGPT models to handle dynamic patient interactions. It also employed agents and tools for enhanced conversational capabilities

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building chatbot to book appointments with doctors, diagnostic tests, clinics, hospitals

Research on building a chatbot to support mental health and neurological patients outside hospitals.

A Retrieval-Augmented Generation (RAG) chatbot that provides accurate, context-aware responses to hospital-related queries using LLMs and document retrieval.

We built 5 modules in this 6-months project: 1. Smart Covid-19 Virus Detector, 2. Heart-Rate Measurement Using Camera, 3. Fever Detection Using IoT Sensors and Devices, 4. Automated HealthCare Chatbot and 5. Web – Application based Smart Covid Hospital website.

This project leverages deep convolutional neural networks to automatically classify the severity of knee osteoarthritis from X-ray images using the Kellgren-Lawrence (KL) grading system. It features a Streamlit-based web app with Grad-CAM visualizations, 3D knee anatomy viewer, DeepSeek R1 chatbot for patient Q&A, and hospital recommendations.

Currently, In India and the world, there are not many people who have the right knowledge of COVID-19. And there is lots of fake news and information spreading all over. We are using Machine learning and web scraping to answer the questions of people. The web app is being used for hospital management wherein the patients can log in to the account and then get information regarding the services of the hospitals. Along with it the patient can make appointments to the doctor after making correct symptom recognition and get to know the disease he/she is suffering from. Along with there is a login page and the contact page. The feedback form helps to collect information regarding the patients and we can use that as a dataset for training and implementing the machine learning algorithm. Also, it consists of a dashboard for the hospital staff to collect information about the patients, their mails and the reports of their illness located at one server to get the information easily and quickly. The chatbot addresses the problems asked by the patients and then keeps a check on the fake news and then we can keep control over the mental well-being of the patients along with their physical health, Hence this is the complete project for the benefit of COVID patients worldwide.

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In this repo, we are using ROS1, RASA chatbot and developing a robot for elderly people that help them to guide their wheelchair to do a specific task.

A web application for Brigham & Women's Hospital, enhancing navigation and service requests. It features an AI chatbot for assistance and a service request management system, improving operational efficiency and user experience in healthcare environments.