How Healthcare Chatbots are Expanding Medical Care
Similarly, conversations between men and machines are not nearly judged by the outcome but by the ease of the interaction. Not only do these responses defeat the purpose of the conversation, but they also make the conversation one-sided and unnatural. A friendly and funny chatbot may work best for a chatbot for new mothers seeking information about their newborns. Still, it may not work for a doctor seeking information about drug dosages or adverse effects.
Without sufficient transparency, deciding how certain decisions are made or how errors may occur reduces the reliability of the diagnostic process. The Black Box problem also poses a concern to patient autonomy by potentially undermining the shared decision-making between physicians and patients [99]. The chatbot’s personalized suggestions are based on algorithms and refined based on the user’s past responses.
These categories are not exclusive, as chatbots may possess multiple characteristics, making the process more variable. Textbox 1 describes some examples of the recommended apps for each type of chatbot but are not limited to the ones specified. Conversational chatbots can be trained on large datasets, including healthcare chatbot the symptoms, mode of transmission, natural course, prognostic factors, and treatment of the coronavirus infection. Bots can then pull info from this data to generate automated responses to users’ questions. Doctors also have a virtual assistant chatbot that supplies them with necessary info – Safedrugbot.
From the perspective of AI developers, Epoch’s study says paying millions of humans to generate the text that AI models will need “is unlikely to be an economical way” to drive better technical performance. “Since we observed this language disparity in their performance, LLM developers should focus on improving accuracy, correctness, consistency, and reliability in other languages,” Jin said. A team of researchers from the College of Computing at Georgia Tech has developed a framework for assessing the capabilities of large language models (LLMs). SmartBot360 combines the best of both worlds, by allowing your organization to create and maintain simple or complex AI chatbots in a DIY fashion, and only request expert consultation when needed. Take this 5-minute assessment to find out where you can optimize your customer service interactions with AI to increase customer satisfaction, reduce costs and drive revenue. The terms chatbot, AI chatbot and virtual agent are often used interchangeably, which can cause confusion.
This can be particularly useful for patients requiring urgent medical attention or having questions outside regular office hours. This global experience will impact the healthcare industry’s dependence on chatbots, and might provide broad and new chatbot implementation opportunities in the future. Chatbots can extract patient information by asking simple questions such as their name, address, symptoms, current doctor, and insurance details.
Artificial intelligence (AI) is at the forefront of transforming numerous aspects of our lives by modifying the way we analyze information and improving decision-making through problem solving, reasoning, and learning. Machine learning (ML) is a subset of AI that improves its performance based on the data provided to a generic algorithm from experience rather than defining rules in traditional approaches [1]. Advancements in ML have provided benefits in terms of accuracy, decision-making, quick processing, cost-effectiveness, and handling of complex data [2]. Chatbots, also known as chatter robots, smart bots, conversational agents, digital assistants, or intellectual agents, are prime examples of AI systems that have evolved from ML. The Oxford dictionary defines a chatbot as “a computer program that can hold a conversation with a person, usually over the internet.” They can also be physical entities designed to socially interact with humans or other robots.
This practice lowers the cost of building the app, but it also speeds up the time to market significantly. This safeguard includes designating people, either by job title or job description, who are authorized to access this data, as well as electronic access control systems, video monitoring, and door locks restricting access to the data. Rasa offers a transparent system of handling and storing patient data since the software developers at Rasa do not have access to the PHI.
The removal of options may slowly reduce the patient’s awareness of alternatives and interfere with free choice [100]. Knowledge domain classification is based on accessible knowledge or the data used to train the chatbot. Under this category are the open domain for general topics and the closed domain focusing on more specific information.
Once upon a time, not all that long ago, visiting the doctor meant sitting in a crowded waiting room. Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space. In 1999, I defined regenerative medicine as the collection of interventions that restore to normal function tissues and organs that have been damaged by disease, injured by trauma, or worn by time.
This medical diagnosis chatbot also offers additional med info for every symptom you input. While chatbots offer many benefits for healthcare providers and patients, several challenges must be addressed to implement them successfully. Healthcare providers must ensure that chatbots are regularly updated and maintained for accuracy and reliability. When using chatbots in healthcare, it is essential to ensure that patients understand how their data will be used and are allowed to opt out if they choose. AI chatbots are used in healthcare to provide patients with a more personalized experience while reducing the workload of healthcare professionals.
Box 2 Characterization of Natural Language Processing (NLP) System Design (Short Title: NLP System Design of the Apps)
Sensely’s offering, Molly, offers the service of using a patient’s entered information to match it with its information repository to provide a suitable diagnosis. However, where Molly differs is that it processes both text and speech as suitable communication. A robust speech recognition feature is baked into the chatbot to process speech in a diction agnostic manner and responds in a suitable manner. To further empower its users, Sensely offers its users the ability to share photos as well as videos.
More specifically, they hold promise in addressing the triple aim of health care by improving the quality of care, bettering the health of populations, and reducing the burden or cost of our health care system. Beyond cancer care, there is an increasing number of creative ways in which chatbots could be applicable to health care. During the COVID-19 pandemic, chatbots were already deployed to share information, suggest behavior, and offer emotional support. You can foun additiona information about ai customer service and artificial intelligence and NLP. They have the potential to prevent misinformation, detect symptoms, and lessen the mental health burden during global pandemics [111].
Facilitate a better patient experience with a healthcare chatbot
Traditional AI chatbots can provide quick customer service, but have limitations. Many rely on rule-based systems that automate tasks and provide predefined responses to customer inquiries. Designing chatbot interfaces for medical information involves training the Natural Language Processing (NLP) model on medical terminology. Implement dynamic conversation pathways for personalized responses, enhancing accuracy. Regularly update the chatbot’s knowledge base to incorporate new medical knowledge. Implement user feedback mechanisms to iteratively refine the chatbot based on insights gathered.
The Promise of Health Chatbots Has Already Failed – Mother Jones – Mother Jones
The Promise of Health Chatbots Has Already Failed – Mother Jones.
Posted: Mon, 06 May 2024 10:02:19 GMT [source]
Modern AI chatbots now use natural language understanding (NLU) to discern the meaning of open-ended user input, overcoming anything from typos to translation issues. Advanced AI tools then map that meaning to the specific “intent” the user wants the chatbot to act upon and use conversational AI to formulate an appropriate response. These AI technologies leverage both machine learning and deep learning—different elements of AI, with some nuanced differences—to develop an increasingly granular knowledge base of questions and responses informed by user interactions.
For medical diagnosis and other healthcare applications, the accuracy and dependability of the chatbot are improved through ongoing development based on user interactions. In recent years, the healthcare landscape has witnessed a transformative integration of technology, with medical chatbots at the forefront of this evolution. Medical chatbots also referred to as health bots or medical AI chatbots, have become instrumental in reshaping patient engagement and accessibility within the healthcare industry. Hence, chatbots in healthcare are reshaping patient interactions and accessibility. Selecting the right platform and technology is critical for developing a successful healthcare chatbot, and Capacity is an ideal choice for healthcare organizations.
Start by defining specific objectives for the chatbot, such as appointment scheduling or symptom checking, aligning with existing workflows. Identify the target audience and potential user scenarios to tailor the chatbot’s functionalities. Integration with electronic health record (EHR) systems streamlines access to relevant patient data, enhancing personalized assistance. Regularly update the chatbot based on user feedback and healthcare advancements to ensure continuous alignment with evolving workflows. The overall functionality, dependability, and user experience of chatbots in the healthcare industry are improved by adding these extra steps to the development and deployment process. Through the adoption of a patient-centered technology strategy, healthcare providers can fully utilize medical chatbots to transform the way patients receive and receive care.
Another unique proposition is in that the chatbot’s repository of information is sourced through resources supplied by cancer patients, their friends and their families to make the whole thing more tailored and intimate. Design the conversational flow of the chatbot to ensure smooth and intuitive interactions with users. Plan the conversation flow, including how the chatbot will greet users, ask questions, and provide responses. Incorporate error handling and fallback mechanisms to handle situations where the chatbot cannot understand or respond to user inquiries. AI chatbots can improve healthcare accessibility for patients who otherwise might not get it.
The more data is included in the training file, the more “intelligent” the bot will be, and the more positive customer experience it’ll provide. A user interface is the meeting point between men and computers; the point where a user interacts with the design. Depending on the type of chatbot, developers use a graphical user interface, voice interactions, or gestures, all of which use different machine learning models to understand human language and generate appropriate responses.
- Using AI and natural language processing, chatbots can help your patients book an appointment or answer a question.
- As the name suggests, CancerChatbot is intended by its developed CSource to serve as a virtual assistant chatbot where patients can seek whatever information they require through a conversation.
- Only six (8%) of apps included in the review had a theoretical/therapeutic underpinning for their approach.
- Motivational interview–based chatbots have been proposed with promising results, where a significant number of patients showed an increase in their confidence and readiness to quit smoking after 1 week [92].
- Beyond the conventional methods of interaction, the incorporation of chatbots in healthcare holds the promise of revolutionizing how patients access information, receive medical advice, and engage with healthcare professionals.
The web-based chatbot ItRuns (ItRunsInMyFamily) gathers family history information at the population level to determine the risk of hereditary cancer [29]. We have yet to find a chatbot that incorporates deep learning to process large and complex data sets at a cellular level. Although not able to directly converse with users, DeepTarget [64] and deepMirGene [65] are capable of performing miRNA and target predictions using expression data with higher accuracy compared with non–deep learning models. With the advent of phenotype–genotype predictions, chatbots for genetic screening would greatly benefit from image recognition. New screening biomarkers are also being discovered at a rapid speed, so continual integration and algorithm training are required. These findings align with studies that demonstrate that chatbots have the potential to improve user experience and accessibility and provide accurate data collection [66].
The ability of chatbots to ensure privacy is especially important, as vast amounts of personal and medical information are often collected without users being aware, including voice recognition and geographical tracking. The public’s lack of confidence is not surprising, given the increased frequency and magnitude of high-profile security breaches and inappropriate use of data [95]. Unlike financial data that becomes obsolete after being stolen, medical data are particularly valuable, as they are not perishable.
The researchers note that accuracy and completeness correlated across difficulty and question type. All authors contributed to the assessment of the apps, and to writing of the manuscript. There were only six (8%) apps that utilized a theoretical or therapeutic framework underpinning their approach, including Cognitive Behavioral Therapy (CBT)43, Dialectic Behavioral Therapy (DBT)44, and Stages of Change/Transtheoretical Model45. Guide patients to the right institutions to help them receive medical assistance quicker. Let them use the time they save to connect with more patients and deliver better medical care. These influencers and health IT leaders are change-makers, paving the way toward health equity and transforming healthcare’s approach to data.
Conference abstracts and grey literature were included when they provided additional information to that available in the published studies. Survivors of cancer, particularly those who underwent treatment during childhood, are more susceptible to adverse health risks and medical complications. Consequently, promoting a healthy lifestyle early on is imperative to maintain quality of life, reduce mortality, and decrease the risk of secondary cancers [87].
The literature review and chatbot search were all conducted by a single reviewer, which could have potentially introduced bias and limited findings. In addition, our review explored a broad range of health care topics, and some areas could have been elaborated upon and explored more deeply. Furthermore, only a limited number of studies were included for each subtopic of chatbots for oncology apps because of the scarcity of studies addressing this topic. Future studies should consider refining the search strategy to identify other potentially relevant sources that may have been overlooked and assign multiple reviews to limit individual bias.
When customers interact with businesses or navigate through websites, they want quick responses to queries and an agent to interact with in real time. Inarguably, this is one of the critical factors that influence customer satisfaction and a company’s brand image (including healthcare organizations, naturally). With standalone chatbots, businesses have been able to drive their customer support experiences, but it has been marred with flaws, quite expectedly.
Three of the apps were not fully assessed because their healthbots were non-functional. Now more than ever, patients find themselves relying on a digital-first approach to healthcare — an arrangement that, at first, might not involve a human on the other end of the exchange. Healthily is an AI-enabled health-tech platform that offers patients personalized health information through a chatbot. From generic tips to research-backed cures, Healthily gives patients control over improving their health while sitting at home.
This allows doctors to process prescription refills in batch or automate them in cases where doctor intervention is not necessary.
In emergency situations, bots will immediately advise the user to see a healthcare professional for treatment. That’s why hybrid chatbots – combining artificial intelligence and human intellect – can achieve better results than standalone AI powered solutions. For example, it may be almost impossible for a healthcare chat bot to give an accurate diagnosis based on symptoms for complex conditions.
Health Hero (Health Hero, Inc), Tasteful Bot (Facebook, Inc), Forksy (Facebook, Inc), and SLOWbot (iaso heath, Inc) guide users to make informed decisions on food choices to change unhealthy eating habits [48,49]. The effectiveness of these apps cannot be concluded, as a more rigorous analysis of the development, evaluation, and implementation is required. Nevertheless, chatbots are emerging as a solution for healthy lifestyle promotion through access and human-like communication while maintaining anonymity. However, healthcare data is often stored in disparate systems that are not integrated. Healthcare providers can overcome this challenge by investing in data integration technologies that allow chatbots to access patient data in real-time.
Multiple Choice, Free-Response, or Both
Nonetheless, chatbots for self-diagnosis are an effective way of advising patients as the first point of contact if accuracy and sensitivity requirements can be satisfied. Chatbots’ robustness of integrating and learning from large clinical data sets, along with its ability to seamlessly communicate with users, contributes to its widespread integration in various health care components. Given the current status and challenges of cancer care, chatbots will likely be a key player in this field’s continual improvement.
Much has changed since then, including new techniques that enabled AI researchers to make better use of the data they already have and sometimes “overtrain” on the same sources multiple times. This dataset contains 690 questions extracted from anonymous consumer queries submitted to MedlinePlus. The answers are sourced from medical references, such as MedlinePlus and DailyMed. XLingHealth contains question-answer pairs that chatbots can reference, which the group hopes will spark improvement within LLMs. Set up an SMS-based chatbot to automatically follow-up with a patient when it is time for their next checkup, or check in with them a certain amount of time after their procedure to make sure they are following proper steps to recover. Most chatbots work well when patients follow the chatbot’s prompts and choices, but often fail when they go off-script.
The specialty of the app is that it is oriented towards patients in the recovery phase and aims to serve as a substitute to frequent need of a doctor for the day to day requirements of their recovery. The app provides an extensively researched selection of diets, exercises and post-cancer practices and the app’s repository of information serves as a handy benchmarking tool as well, should the user want to check the safety of a product. The crowning glory of the app is the offering of an on-call oncologist 24 hours a day 7 days a week for its users should a need for a consultation arise. ChatGPT and similar large language models would be the next big step for artificial intelligence incorporating into the healthcare industry.
Step 2. Choose the right platform and technology:
This is particularly important in relation to health care, an area where clinical practice guidelines, best practices, and safety data are continuously changing. The lack of real-time updates to the content of chatbots could result in people receiving out-of-date information in response to their queries. The same can be true for human-to-human interactions; however, a health care provider does have the ability to access up-to-date information in real time, whereas an AI chatbot does not.
Whenever a patient strikes up a conversation with a medical representative who may sound human but underneath is an intelligent conversational machine — we see a healthcare chatbot in the medical field in action. Telemedicine uses technology to provide healthcare services remotely, while chatbots are AI-powered virtual assistants that provide personalized patient support. They offer a powerful combination to improve patient outcomes and streamline healthcare delivery. For example, chatbots can schedule appointments, answer common questions, provide medication reminders, and even offer mental health support. These chatbots also streamline internal support by giving these professionals quick access to information, such as patient history and treatment plans. Artificial Intelligence (AI) and automation have rapidly become popular in many industries, including healthcare.
Healthcare providers can overcome this challenge by working with experienced UX designers and testing chatbots with diverse patients to ensure that they meet their needs and expectations. As such, there are concerns about how chatbots collect, store, and use patient data. Healthcare providers must ensure that privacy laws and ethical standards handle patient data. In this article, we will explore how chatbots in healthcare can improve patient engagement and experience and streamline internal and external support. With a lack of proper input data, there is the ongoing risk of “hallucinations,” delivering inaccurate or irrelevant answers that require the customer to escalate the conversation to another channel. Any advantage of a chatbot can be a disadvantage if the wrong platform, programming, or data are used.
Promising progress has also been made in using AI for radiotherapy to reduce the workload of radiation staff or identify at-risk patients by collecting outcomes before and after treatment [70]. An ideal chatbot for health care professionals’ use would be able to accurately detect diseases and provide the proper course of recommendations, which are functions currently limited by time and budgetary constraints. Continual algorithm training and updates would be necessary because of the constant improvements in current standards of care.
The Indian government also launched a WhatsApp-based interactive chatbot called MyGov Corona Helpdesk that provides verified information and news about the pandemic to users in India. Another point to consider is whether your medical AI chatbot will be integrated with existing software systems and applications like EHR, telemedicine platforms, etc. These are the tech measures, policies, and procedures that protect and control access to electronic health data. Furthermore, this rule requires that workforce members only have access to PHI as appropriate for their roles and job functions. Furthermore, Rasa also allows for encryption and safeguarding all data transition between its NLU engines and dialogue management engines to optimize data security.
The language was restricted to “English” for the iOS store and “English” and “English (UK)” for the Google Play store. The search was further limited using the Interactive Advertising Bureau (IAB) categories “Medical Health” and “Healthy Living”. The IAB develops industry standards to support categorization in the digital advertising industry; 42Matters labeled apps using these standards40. Relevant apps on the iOS Apple store were identified; then, the Google Play store was searched with the exclusion of any apps that were also available on iOS, to eliminate duplicates.
Users are permitted to make copies of this document for non-commercial purposes only, provided it is not modified when reproduced and appropriate credit is given to CADTH and its licensors. The HIPAA Security Rule requires that you identify all the sources of PHI, including external sources, and all human, technical, and environmental threats to the safety of PHI in your company. The Rule requires that your company design a mechanism that encrypts all electronic PHI when necessary, both at rest or in transit over electronic communication tools such as the internet. Furthermore, the Security Rule allows flexibility in the type of encryption that covered entities may use. The Security Rule describes the physical safeguards as the physical measures, policies, and processes you have to protect a covered entity’s electronic PHI from security violations. Rasa is also available in Docker containers, so it is easy for you to integrate it into your infrastructure.
Enterprise-grade, self-learning generative AI chatbots built on a conversational AI platform are continually and automatically improving. They employ algorithms that automatically learn from past interactions how best to answer questions and improve conversation flow routing. Infused with advanced AI capabilities, medical chatbot play a pivotal role in the initial assessment of symptoms.
That’ll help your patients get a seamless and convenient experience when they need it. Our review suggests that healthbots, while potentially transformative in centering care around the user, are in a nascent state of development and require further research on development, automation, and adoption for a population-level health impact. An AI-fueled platform that supports patient engagement and improves communication in your healthcare organization. This means that hospitals could leverage digital humans as health assistants, capable of providing empathetic, around-the-clock aid to patients, particularly before or after their surgery. Chatbot becomes a vital point of communication and information gathering at unforeseeable times like a pandemic as it limits human interaction while still retaining patient engagement.
Upon downloading the app, the user provides all necessary information that the app’s artificially intelligent models process and drives the patient towards a consultation with the in-built symptom checker. After, the user is provided with a diagnostic report and the option to engage in a video-based consultation with a practitioner to take the necessary next steps as required. With rapid advancements in the field of artificial intelligence and machine learning, it is quickly affecting the healthcare industry as well. One of the ways AI algorithms have adapted for the industry is through chatbots, or programs coded to carry out a textual conversation with the user using context cues and pre-set responses. Chatbots have found a wide user base across all sorts of industries thanks to their handy and effective ability to automate tasks and save time for both the service’s user and provider, and the healthcare industry is no different. Many healthcare chatbots using artificial intelligence already exist in the healthcare industry.
Most chatbots use one data source of keywords to detect and to have certain responses to those keywords, but this does not work well in cases where patients do not use provided keywords. Patients expect immediate replies to their requests nowadays with chatbots being used in so many non-healthcare https://chat.openai.com/ businesses. A chatbot can either provide the answer through the chatbot or direct them to a page with an answer. Any software simulating human conversation, whether powered by traditional, rigid decision tree-style menu navigation or cutting-edge conversational AI, is a chatbot.
Improved patient outcomes
A clearly defined scope guarantees that the chatbot’s skills correspond with the intended results, whether those outcomes be expediting appointment scheduling, offering medical information, or aiding in medical diagnosis. The groundwork for a focused and efficient conversational AI in healthcare is laid by this action. In this comprehensive guide, we will explore the step-by-step process of developing and implementing medical chatbot, shedding light on their crucial role in improving patient engagement and healthcare accessibility. Available through Facebook Messenger, Kik or Skype, the chatbot acquires required information such as prevalent medical conditions, any medications being taken (along with their timings, dosages etc.), and more from the user. It will then process that information and appropriately remind the user to take the medicines at the scheduled time, along with instructions on what to do if that time is missed. In addition to the above, Florence also goes the extra mile and reminds users to log their body weight, menstrual periods, moods etc. to process and analyze over a period, so that information can be provided when required.
This report is not a systematic review and does not involve critical appraisal or include a detailed summary of study findings. It is not intended to provide recommendations for or against the use of the technology and focuses only on AI chatbots in health care settings, not broader used of AI within health care. The prevalence of cancer is increasing along with the number of survivors of cancer, partly because of improved treatment techniques and early detection [77]. A number of these individuals require support after hospitalization or treatment periods.
Capacity’s conversational AI platform enables graceful human handoffs and intuitive task management via a powerful workflow automation suite, robust developer platform, and flexible database that can be deployed anywhere. Cem’s work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence. Many of those debating the pros and cons of AI agree that its awesome potential cannot be left only in the hands of those who want to manipulate it for power or for profit. This will require regulation to ensure that the technology is accessible to everyone on an equal basis.
Cem’s hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks. A tool to assist people in removing attached ticks and seeking health care, if appropriate, after a tick bite. The online mobile-friendly tool asks a series of questions covering topics such as tick attachment time and symptoms.
Chatbots Are Poor Multilingual Healthcare Consultants, Study Finds News Center – Georgia Tech News Center
Chatbots Are Poor Multilingual Healthcare Consultants, Study Finds News Center.
Posted: Wed, 15 May 2024 07:00:00 GMT [source]
There has been one systematic review of commercially available apps; this review focused on features and content of healthbots that supported dementia patients and their caregivers34. To our knowledge, no review has been published examining the landscape of commercially available and consumer-facing healthbots across all health domains and characterized the NLP system design of such apps. This review aims to classify the types of healthbots available on the app store (Apple iOS and Google Play app stores), their contexts of use, as well as their NLP capabilities.
Access to patient information enables chatbots to tailor interactions, providing contextually relevant assistance and information. CancerChatbot is unique from other chatbots in the sense that unlike most others which are a standalone application or web-based tool, CancerChatbot is delivered through Facebook Messenger. As the name suggests, CancerChatbot is intended by its developed CSource to serve as a virtual assistant chatbot where patients can seek whatever information they require through a conversation.
Chatbots have been incorporated into health coaching systems to address health behavior modifications. For example, CoachAI and Smart Wireless Interactive Health System used chatbot technology to track patients’ progress, provide insight to physicians, and suggest suitable activities [45,46]. Another app is Weight Mentor, which provides self-help motivation for weight loss maintenance and allows for open conversation without being affected by emotions [47].
The health bot uses machine learning algorithms to adapt to new data, expanding medical knowledge, and changing user needs. In the first stage, a comprehensive needs analysis is conducted to pinpoint particular healthcare domains that stand to gain from a conversational Chat GPT AI solution. Comprehending the obstacles encountered by healthcare providers and patients is crucial for customizing the functionalities of the chatbot. This stage guarantees that the medical chatbot solves practical problems and improves the patient experience.
Healthcare chatbots give patients an easy way to access healthcare information and services. Chatbots provide instant conversational responses and make connecting simple for patients. And when implemented properly, they can help care providers to surpass patient expectations and improve patient outcomes. Today’s healthcare chatbots are obviously far more reliable, effective, and interactive. As advancements in AI are ever evolving and ameliorating, chatbots will inevitably perform a range of complex activities and become an indispensable part of many industries, mainly, healthcare. The search approach was customized to retrieve a limited set of results, balancing comprehensiveness with relevancy.
Rasa stack provides you with an open-source framework to build highly intelligent contextual models giving you full control over the process flow. Conversely, closed-source tools are third-party frameworks that provide custom-built models through which you run your data files. With these third-party tools, you have little control over the software design and how your data files are processed; thus, you have little control over the confidential and potentially sensitive patient information your model receives. Chatbots are revolutionizing social interactions on a large scale, with business owners, media companies, automobile industries, and customer service representatives employing these AI applications to ensure efficient communication with their clients. Before designing a conversational pathway for an AI driven healthcare bot, one must first understand what makes a productive conversation.
