Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review PMC

Moreover, integrating RPA or other automation solutions with chatbots allows for automating insurance claims processing and healthcare billing. Chatbot algorithms are trained on massive healthcare data, including disease symptoms, diagnostics, markers, and available treatments. Public datasets are used to continuously train chatbots, such as COVIDx for COVID-19 diagnosis, and Wisconsin Breast Cancer Diagnosis (WBCD). Considering their capabilities and limitations, check out the selection of easy and complicated tasks for artificial intelligence chatbots in the healthcare industry. A thorough research of LLMs is recommended to avoid possible technical issues or lawsuits when implementing a new artificial intelligence chatbot. For example, ChatGPT 4 and ChatGPT 3.5 LLMs are deployed on cloud servers that are located in the US.

Chatbots are unable to efficiently cope with these errors because of the lack of common sense and the inability to properly model real-world knowledge [105]. Another factor that contributes to errors and inaccurate predictions is the large, noisy data sets used to train modern models because large quantities of high-quality, representative data are often unavailable [58]. In addition to the concern of accuracy and validity, addressing clinical utility and effectiveness of improving patients’ quality of life is just as important.

In this comprehensive guide, we‘ll explore six high-impact chatbot applications in healthcare, real-world examples, implementation best practices, evaluations of leading solutions, and predictions for the future. Read on to gain valuable insights you can apply to your healthcare chatbot initiatives. Chatbots can handle several inquiries and tasks simultaneously without added human resources. This can save you on staffing and admin overhead while still letting you provide the quality of care your patients expect.

Chatbots do not get tired, fatigued, or sick, and they do not need to sleep; they are cost-effective to operate and can run 24 hours a day, which is especially useful for patients who may have medical concerns outside of their doctor’s operating hours. Chatbots can also communicate in multiple different languages to better suit the needs of individual patients. You can foun additiona information about ai customer service and artificial intelligence and NLP. This study aimed to investigate the perceptions of physicians regarding the use of health care chatbots, including their benefits, challenges, and risks to patients. AI and ML have advanced at an impressive rate and have revealed the potential of chatbots in health care and clinical settings.

UK health authorities have recommended apps, such as Woebot, for those suffering from depression and anxiety (Jesus 2019). Pasquale (2020, p. 46) pondered, ironically, that cheap mental health apps are a godsend for health systems pressed by austerity cuts, such as Britain’s National Health Service. Unfortunately, according to a study in the journal Evidence Based Mental Health, the true clinical value of most apps was ‘impossible to determine’.

Step 3: Fuse the best of human and AI

We suggest that new ethico-political approaches are required in professional ethics because chatbots can become entangled with clinical practices in complex ways. It is difficult to assess the legitimacy of particular applications and their underlying business interests using concepts drawn from universal AI ethics or traditional professional ethics inherited from bioethics. Insufficient consideration regarding the implementation of chatbots in health care can lead to poor professional practices, creating long-term side effects and harm for professionals and their patients. While we acknowledge that the benefits of chatbots can be broad, whether they outweigh the potential risks to both patients and physicians has yet to be seen.

They are considered to be ground-breaking technologies in customer relationships. Since healthcare chatbots can be on duty tirelessly both day and night, they are an invaluable addition to the care of the patient. Theoretically, in some instances, chatbots may be better suited to help patient needs than a human physician because they have no biological gender, age, or race and elicit no bias toward patient demographics.

This allows them to take on even more complex responsibilities, such as recognizing symptoms and even making diagnoses. With such improvements, the future of chatbots in healthcare looks quite bright. The design principles of most health technologies are based on the idea that technologies should mimic human decision-making capacity. These systems are computer programmes that are ‘programmed to try and mimic a human expert’s decision-making ability’ (Fischer and Lam 2016, p. 23). Thus, their function is to solve complex problems using reasoning methods such as the if-then-else format.

• Beginning with primary healthcare services, the chatbot industry could gain experience and help develop more reliable solutions.
• However, one of the downsides is patients’ overconfidence in the ability of chatbots, which can undermine confidence in physician evaluations.
• In addition, voice and image recognition should also be considered, as most chatbots are still text based.

Doctors would expect essential info delivered in the appropriate medical lexicon. Hyro is an adaptive communications platform that replaces common-place intent-based AI chatbots with language-based conversational AI, built from NLU, knowledge graphs, and computational linguistics. Woebot is a chatbot designed by researchers at Stanford University to provide mental health assistance using cognitive behavioral therapy (CBT) techniques. People who suffer from depression, anxiety disorders, or mood disorders can converse with this chatbot, which, in turn, helps people treat themselves by reshaping their behavior and thought patterns.

The authors would like to thank all the participants, project members, supporters, and researchers at Klick Inc for the successful development, implementation, and evaluation of this research. The authors would also like to acknowledge Gaurav Baruah and Peter Leimbigler for their helpful comments on the research design and survey. This research was internally funded and received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. 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. All the tools you use on Rasa are hosted in your HIPAA-complaint on-premises system or private data cloud, which guarantees a high level of data privacy since all the data resides in your infrastructure.

Mathematical or statistical probability in medical diagnosis has become one of the principal targets, with the consequence that AI is expected to improve diagnostics in the long run. Hacking (1975) has reminded us of the dual nature between statistical probability and epistemic probability. Statistical probability is concerned with ‘stochastic laws of chance processes’, while epistemic probability gauges ‘reasonable degrees of belief in propositions quite devoid of statistical background’ (p. 12). Epistemic probability concerns our possession of knowledge, or information, meaning how much support is given by all the available evidence.

Types of Chatbots and Their Applications

In the last decade, medical ethicists have attempted to outline principles and frameworks for the ethical deployment of emerging technologies, especially AI, in health care (Beil et al. 2019; Mittelstadt 2019; Rigby 2019). As conversational agents have gained popularity during the COVID-19 pandemic, medical experts have been required to respond more quickly to the legal and ethical aspects of chatbots. Task-oriented chatbots follow these models of thought in a precise manner; their functions are easily derived from prior expert processes performed by humans. However, more conversational bots, for example, those that strive to help with mental illnesses and conditions, cannot be constructed—at least not easily—using these thought models. This requires the same kind of plasticity from conversations as that between human beings.

Ada Health is a popular healthcare app that understands symptoms and manages patient care instantaneously with a reliable AI-powered database. A healthcare chatbot also sends out gentle reminders to patients for the consumption of medicines at the right time https://chat.openai.com/ when requested by the doctor or the patient. According to an MGMA Stat poll, about 49% of medical groups said that the rates of ‘no-shows‘ soared since 2021. No-show appointments result in a considerable loss of revenue and underutilize the physician’s time.

For example, in the field of psychology, so-called ‘script theory’ provided a formal framework for knowledge (Fischer and Lam 2016). Thus, as a formal model that was already in use, it was relatively easy to turn it into algorithmic form. These expert systems were part of the automated decision-making (ADM) process, that is, a process completely devoid of human involvement, which makes final decisions on the basis of the data it receives (European Commission 2018, p. 20). Conversely, health consultation chatbots are partially automated proactive decision-making agents that guide the actions of healthcare personnel. Chatbots experience the Black

Box problem, which is similar to many computing systems programmed using ML that are trained on massive data sets to produce multiple layers of connections.

A medical bot is created with the help of machine learning and large language models (LLMs). Pasquale (2020, p. 57) has reminded us that AI-driven systems, including chatbots, mirror the successes and failures of clinicians. However, machines do not have the human capabilities of prudence and practical wisdom or the flexible, interpretive capacity to correct mistakes and wrong decisions. As a result of self-diagnosis, physicians may have difficulty convincing patients of their potential preliminary, chatbot-derived misdiagnosis. This level of persuasion and negotiation increases the workload of professionals and creates new tensions between patients and physicians.

This number will jump to $65 million by 2032, with an annual growth rate of 16.98%. It’s just that healthcare has received a powerful tool, mastered it, and plans to use it in the future. Rapid diagnoses by chatbots can erode diagnostic practice, which requires practical wisdom and collaboration between different specialists as well as close communication with patients. HCP expertise relies on the intersubjective circulation of knowledge, that is, a pool of dynamic knowledge and the intersubjective criticism of data, knowledge and processes. I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good.

In addition to the content, some apps allowed for customization of the user interface by allowing the user to pick their preferred background color and image. Seventy-four (53%) apps targeted patients with specific illnesses or diseases, sixty (43%) targeted patients’ caregivers or healthy individuals, and six (4%) targeted healthcare providers. The total sample size exceeded seventy-eight as some apps had multiple target populations. Some experts also believe doctors will recommend chatbots to patients with ongoing health issues. In the future, we might share our health information with text bots to make better decisions about our health.

The chatbot needs to understand natural language and respond accurately to user inquiries. Implementing healthcare chatbots can be a cost-effective solution for healthcare providers. Finally, physicians were asked how likely it would be, in the future, for health care chatbots to play a more significant role in patients’ health than their HCP. A total of 49% (49/100) expressed that this would be very likely (15%, 15/100) or somewhat likely (34%, 34/100) to happen, whereas 25% (25/100) expressed that this would be somewhat unlikely (15%, 15/100) or very unlikely (10%, 10/100) to happen. Of the 30 participants who have used health care chatbots previously, 4 (13%) were very satisfied, 10 (33%) were somewhat satisfied, 8 (27%) were neither satisfied nor dissatisfied, and 8 (27%) were somewhat dissatisfied with their application. Of all the physicians in the survey, 18% (18/100) stated that their patients use health care chatbots (24%, 24/100, stated that patients did not use them), but the majority (58%, 58/100) were unsure or did not know whether their patients use them.

For example, the development of the Einstein app as a web-based physics teacher enables interactive learning and evaluations but is still far from being perfect [114]. Given chatbots’ diverse applications in numerous aspects of health care, further research and interdisciplinary collaboration to advance this technology could revolutionize the practice of medicine. Health-focused apps with chatbots (“healthbots”) have a critical role in addressing gaps in quality healthcare. There is limited evidence on how such healthbots are developed and applied in practice. Our review of healthbots aims to classify types of healthbots, contexts of use, and their natural language processing capabilities.

In the case of Tessa, a wellness chatbot provided harmful recommendations due to errors in the development stage and poor training data. With so many algorithms and tools around, knowing the different types of chatbots in healthcare is key. This will help you to choose the right tools or find the right experts to build a chat agent that suits your users’ needs. This way, clinical chatbots help medical workers allocate more time to focus on patient care and more important tasks.

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]. Chatbots are now equipped with advanced conversational AI capabilities to understand complex questions, engage in natural dialogue, and build rapport with users. They leverage data analytics to provide personalized health insights and recommendations customized to individual needs and preferences.

Patients love speaking to real-life doctors, and artificial intelligence is what makes chatbots sound more human. In fact, some chatbots with complex self-learning algorithms can successfully maintain in-depth, nearly human-like conversations. Further refinements and large-scale implementations are still required to determine the benefits across different populations and sectors in health care [26]. Although overall satisfaction is found to be relatively high, there is still room for improvement by taking into account user feedback tailored to the patient’s changing needs during recovery. In combination with wearable technology and affordable software, chatbots have great potential to affect patient monitoring solutions. While chatbots offer many benefits for healthcare providers and patients, several challenges must be addressed to implement them successfully.

Textbox 1 describes some examples of the recommended apps for each type of chatbot but are not limited to the ones specified. Chatbots can help patients manage their health more effectively, leading to better outcomes and a higher quality of life. These bots can help patients stay on track with their healthcare goals and manage chronic conditions more effectively by providing personalized support and assistance.

To our knowledge, our study is the first comprehensive review of healthbots that are commercially available on the Apple iOS store and Google Play stores. Another review conducted by Montenegro et al. developed a taxonomy of healthbots related to health32. Both of these reviews focused on healthbots that were available in scientific literature only and did not include commercially available apps. Our study leverages and further develops the evaluative criteria developed by Laranjo et al. and Montenegro et al. to assess commercially available health apps9,32. We identified 78 healthbot apps commercially available on the Google Play and Apple iOS stores. Healthbot apps are being used across 33 countries, including some locations with more limited penetration of smartphones and 3G connectivity.

This data will train the chatbot in understanding variants of a user input since the file contains multiple examples of single-user intent. 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. Just as effective human-to-human conversations largely depend on context, a productive conversation with a chatbot also heavily depends on the user’s context. Any chatbot you develop that aims to give medical advice should deeply consider the regulations that govern it. There are things you can and cannot say, and there are regulations on how you can say things. Navigating yourself through this environment will require legal counsel to guide you as you build this portion of your bot to address these different chatbot use cases in healthcare.

Assessing symptoms, consulting, renewing prescriptions, and booking appointments — this isn’t even an entire list of what modern healthcare chatbots can do for healthcare entities. They never get tired and help reduce the workload for doctors, which makes patient care better. One of the key elements of expertise and its recognition is that patients and others can trust the opinions and decisions offered by the expert/professional. However, in the case of chatbots, ‘the most important factor for explaining trust’ (Nordheim et al. 2019, p. 24) seems to be expertise. People can trust chatbots if they are seen as ‘experts’ (or as possessing expertise of some kind), while expertise itself requires maintaining this trust or trustworthiness. Chatbot users (patients) need to see and experience the bots as ‘providing answers reflecting knowledge, competence, and experience’ (p. 24)—all of which are important to trust.

Top 10 Innovative Data Center Automation Tools in 2023

Chatbots are artificial intelligence (AI) programs that can generate and retrieve information for the interaction with human users via text or computer voice generation. Participants who took part in the survey were sampled from a large database of physicians who have previously agreed to take part in market research. The survey was administered by Sermo [28], a private social media network for licensed physicians, who randomly selected registered physicians within their panel across the United States. The Sermo research network comprises over 400,000 registered physicians in the United States, representing roughly 40% of the US physician population [19].

With the growing spread of the disease, there comes a surge of misinformation and diverse conspiracy theories, which could potentially cause the pandemic curve to keep rising. Therefore, it has become necessary to leverage digital tools that disseminate authoritative healthcare information to people across the globe. Another point to consider is whether your medical AI chatbot will be integrated with existing software systems and applications like EHR, telemedicine platforms, etc. Healthcare chatbot development can be a real challenge for someone with no experience in the field. Patients can naturally interact with the bot using text or voice to find medical services and providers, schedule an appointment, check their eligibility, and troubleshoot common issues using FAQ for fast and accurate resolution. The higher the intelligence of a chatbot, the more personal responses one can expect, and therefore, better customer assistance.

Eligible apps were those that were health-related, had an embedded text-based conversational agent, available in English, and were available for free download through the Google Play or Apple iOS store. Apps were assessed using an evaluation framework addressing chatbot characteristics and natural language processing features. Most healthbots are patient-facing, available on a mobile interface and provide a range of functions including health education and counselling support, assessment of symptoms, and assistance with tasks such as scheduling. Most of the 78 apps reviewed focus on primary care and mental health, only 6 (7.59%) had a theoretical underpinning, and 10 (12.35%) complied with health information privacy regulations. Our assessment indicated that only a few apps use machine learning and natural language processing approaches, despite such marketing claims. Most apps allowed for a finite-state input, where the dialogue is led by the system and follows a predetermined algorithm.

This «AI-powered health assistant» will integrate seamlessly with each care team to fully support the patient‘s physical, mental, social and financial health needs. According to research by Accenture, scaling healthcare chatbots could result in over $3 billion in annual cost savings for the US healthcare system alone by 2023. Another study found that 70% of healthcare organizations are currently piloting or planning to pilot chatbots. 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.

The United States had the highest number of total downloads (~1.9 million downloads, 12 apps), followed by India (~1.4 million downloads, 13 apps) and the Philippines (~1.25 million downloads, 4 apps). Details on the number of downloads and app across the 33 countries are available in Appendix 2. Only ten apps (12%) stated that they were HIPAA compliant, and three (4%) were Child Online Privacy and Protection Act (COPPA)-compliant.

An AI chatbot can be integrated with third-party software, enabling them to deliver proper functionality. How do we deal with all these issues when developing a clinical chatbot for healthcare? The CodeIT team has solutions to tackle the major text bot drawbacks, perfect for businesses like yours. We adhere to HIPAA and GDPR compliance standards to ensure data security and privacy. Our developers can create any conversational agent you need because that’s what custom healthcare chatbot development is all about.

• However, there are different levels of maturity to a conversational chatbot – not all of them offer the same depth of conversation.
• It’s just that healthcare has received a powerful tool, mastered it, and plans to use it in the future.
• This allows them to take on even more complex responsibilities, such as recognizing symptoms and even making diagnoses.
• These bots can help patients stay on track with their healthcare goals and manage chronic conditions more effectively by providing personalized support and assistance.
• This free AI-enabled chatbot allows you to input your symptoms and get the most likely diagnoses.

Similarly, conversational style for a healthcare bot for people with mental health problems such as depression or anxiety must maintain sensitivity, respect, and appropriate vocabulary. Chatbots can be accessed anytime, providing patients support outside regular office hours. 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.

Improving diagnostic accuracy

The chatbot’s NLP capabilities analyze the user’s input to understand their intent and desired outcome. This involves identifying keywords, phrases, and context to interpret the user’s query or request. New technologies may form new gatekeepers of access to specialty care or entirely usurp human doctors in many patient cases.

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. The use of chatbots in healthcare helps improve the performance of medical staff by enabling automation. They can automate bothersome and time-consuming tasks, like appointment scheduling or consultation.

There are many other reasons to build a healthcare chatbot, and you’ll find most of them here. The insights we’ll share are grounded on our 10-year experience and reflect Chat PG our expertise in healthcare software development. A report by Precedence Research noted that the market value for AI chatbots in healthcare stood at $4.3 million in 2023.

As the AI field lacks diversity, bias at the level of the algorithm and modeling choices may be overlooked by developers [102]. In a study using 2 cases, differences in prediction accuracy were shown concerning gender and insurance type for intensive care unit mortality and psychiatric readmissions [103]. On a larger scale, this may exacerbate barriers to health care for minorities or underprivileged individuals, leading to worse health outcomes.

While there were 78 apps in the review, accounting for the multiple categorizations, this multi-select characterization yielded a total of 83 (55%) counts for one or more of the focus areas. To facilitate this assessment, we develop and present an evaluative framework that classifies the key characteristics of healthbots. chatbot in healthcare Concerns over the unknown and unintelligible “black boxes” of ML have limited the adoption of NLP-driven chatbot interventions by the medical community, despite the potential they have in increasing and improving access to healthcare. Further, it is unclear how the performance of NLP-driven chatbots should be assessed.

So far, there has been scant discussion on how digitalisation, including chatbots, transform medical practices, especially in the context of human capabilities in exercising practical wisdom (Bontemps-Hommen et al. 2019). As conversational AI continues advancing, measurable benefits like these will accelerate chatbot adoption exponentially. By thoughtfully implementing chatbots aligned to organizational goals, healthcare providers can elevate patient experiences and clinical outcomes to new heights. The transformative power of AI to augment clinicians and improve healthcare access is here – the time to implement chatbots is now.

As this study was the first of its kind and exploratory in nature to study the subjective opinions of physicians, no explicit statistical hypotheses were being evaluated. The sample size of 100 was arbitrarily chosen to gather a preliminary viewpoint of physicians’ perspectives of chatbots in health care and would yield approximately a 9.8% margin of error with a 95% CI of the entire US physician population. Despite the initial chatbot hype dwindling down, medical chatbots still have the potential to improve the healthcare industry. The three main areas where they can be particularly useful include diagnostics, patient engagement outside medical facilities, and mental health. At least, that’s what CB Insights analysts are bringing forward in their healthcare chatbot market research, generally saying that the future of chatbots in the healthcare industry looks bright. The prevalence of cancer is increasing along with the number of survivors of cancer, partly because of improved treatment techniques and early detection [77].

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. Privacy threats may break the trust that is essential to the therapeutic physician–patient relationship and inhibit open communication of relevant clinical information for proper diagnosis and treatment [96]. Telemedicine uses technology to provide healthcare services remotely, while chatbots are AI-powered virtual assistants that provide personalized patient support.

Now that we’ve gone over all the details that go into designing and developing a successful chatbot, you’re fully equipped to handle this challenging task. We’re app developers in Miami and California, feel free to reach out if you need more in-depth research into what’s already available on the off-the-shelf software market or if you are unsure how to add AI capabilities to your healthcare chatbot. Let’s create a contextual chatbot called E-Pharm, which will provide a user – let’s say a doctor – with drug information, drug reactions, and local pharmacy stores where drugs can be purchased. The first step is to create an NLU training file that contains various user inputs mapped with the appropriate intents and entities. 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. Just as patients seeking information from a doctor would be more comfortable and better engaged by a friendly and compassionate doctor, conversational styles for chatbots also have to be designed to embody these personal qualities.

Chatbots can be exploited to automate some aspects of clinical decision-making by developing protocols based on data analysis. 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. Conversational chatbots use natural language processing (NLP) and natural language understanding (NLU), applications of AI that enable machines to understand human language and intent. With the vast number of algorithms, tools, and platforms available, understanding the different types and end purposes of these chatbots will assist developers in choosing the optimal tools when designing them to fit the specific needs of users. These categories are not exclusive, as chatbots may possess multiple characteristics, making the process more variable.

These platforms have different elements that developers can use for creating the best chatbot UIs. Almost all of these platforms have vibrant visuals that provide information in the form of texts, buttons, and imagery to make navigation and interaction effortless. However, humans rate a process not only by the outcome but also by how easy and straightforward the process is. Similarly, conversations between men and machines are not nearly judged by the outcome but by the ease of the interaction.

If you wish to know anything about a particular disease, a healthcare chatbot can gather correct information from public sources and instantly help you. Healthcare chatbots enable you to turn all these ideas into a reality by acting as AI-enabled digital assistants. It revolutionizes the quality of patient experience by attending to your patient’s needs instantly.

The healthcare industry incorporates chatbots in its ecosystem to streamline communication between patients and healthcare professionals, prevent unnecessary expenses and offer a smooth, around-the-clock helping station. Certainly, chatbots can’t match the expertise and care provided by seasoned doctors or qualified nurses because their knowledge bases might be constrained, and their responses sometimes fall short of user expectations. Each type of chatbot plays a unique role in the healthcare ecosystem, contributing to improved patient experience, enhanced efficiency, and personalized care. With the continuous progression of technology, we are likely to witness the emergence of increasingly innovative chatbots.

Research indicates chatbots improve retention of health education content by over 40% compared to traditional written materials. Travel nurses or medical billers can use AI chatbots to connect with providers when looking for new assignments. Bots can assess the availability of job postings, preferences, and qualifications to match them with opportunities. Whether they need a refill or simply a reminder to take their prescription, the bot can help. This is helpful in IDing side effects, appropriate dosages, and how they might interact with other medications.

Although, if you’re looking for a basic chatbot assisting your website visitors, we advise you to take a look at some existing solutions like Smith.ai, Acobot, or Botsify. 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. We recommend using ready-made SDKs, libraries, and APIs to keep the chatbot development budget under control. This practice lowers the cost of building the app, but it also speeds up the time to market significantly.

In addition, health chatbots have been deemed promising in terms of consulting patients in need of psychotherapy once COVID-19-related physical distancing measures have been lifted. Healthy diets and weight control are key to successful disease management, as obesity is a significant risk factor for chronic conditions. Chatbots have been incorporated into health coaching systems to address health behavior modifications.