Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care PMC

They provide personalized, easy-to-understand information about diseases, treatments, and preventive measures. This continuous education empowers patients to make informed health decisions, promotes preventive care, and encourages a proactive approach to health. Two-thirds of the chatbots in this review used predefined rules and decision trees to generate their responses, while the remaining chatbots used artificial intelligence. In contrast to rule-based chatbots, artificial intelligence chatbots can generate responses to complicated queries and enable users to control the conversation [13]. Artificial intelligence chatbots can exhibit more empathetic behaviors and humanlike filler language than rule-based chatbots [19]. This may make artificial intelligence chatbots more effective in building rapport with users, thereby improving their mental health [42].

Hopefully, after reviewing these samples of the best healthcare chatbots above, you’ll be inspired by how your chatbot solution for the healthcare industry can enhance provider/patient experiences. Healthcare chatbots are AI-enabled digital assistants that allow patients to assess their health and get reliable results anywhere, anytime. It manages appointment scheduling and rescheduling while gently reminding patients of their upcoming visits to the doctor. It saves time and money by allowing patients to perform many activities like submitting documents, making appointments, self-diagnosis, etc., online. Artificial intelligence, natural language processing (NLP), neuro-symbolic AI, and other groundbreaking innovations are revolutionizing multiple industries today. These novel technologies penetrate various areas of the healthcare system and find ready applications in hospitals, research labs, nursing homes, pharmacies, and doctor practices.

For instance, severity of depression was measured using PHQ-9, Beck Depression Inventory II, or Hospital Anxiety and Depression Scale. Further, while some studies assessed outcomes before and after interventions, other studies examined them only after interventions. The field would benefit from future studies using a common set of outcome measures to ease comparison and interpretation of results between studies. Only one study assessed the long-term effectiveness and safety of chatbots, where participants were followed for 12 weeks.

However, these kinds of quantitative methods omitted the complex social, ethical and political issues that chatbots bring with them to health care. 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.

Google has also expanded this opportunity for tech companies to allow them to use its open-source framework to develop AI chatbots. The challenge here for software developers is to keep training chatbots on COVID-19-related verified updates and research data. As researchers uncover new symptom patterns, these details need to be integrated into the ML training data to enable a bot to make an accurate assessment of a user’s symptoms at any given time. Recently the World Health Organization (WHO) partnered with Ratuken Viber, a messaging app, to develop an interactive chatbot that can provide accurate information about COVID-19 in multiple languages. With this conversational AI, WHO can reach up to 1 billion people across the globe in their native languages via mobile devices at any time of the day. 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.

Study Eligibility Criteria

It’s imperative to rigorously train AI and mitigate biases prior to deploying chatbots in the healthcare domain. There are risks involved when patients are expected to self-diagnose, such as a misdiagnosis provided by the chatbot or patients potentially lacking an understanding of the diagnosis. If experts lean on the false ideals of chatbot capability, this can also lead to patient overconfidence and, furthermore, ethical problems. 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. Since the 1950s, there have been efforts aimed at building models and systematising physician decision-making.

One in 4 adults and 1 in 10 children are likely to be affected by mental health problems annually [2]. Mental illness has a significant impact on the lives of millions of people and a profound impact on the community and economy. Mental disorders impair quality of life and are considered one of the most common causes of disability [3]. Mental disorders are predicted to cost $16 trillion globally between 2011 and 2030 due to lost labor and capital output [4]. Pranjal Mehta is the Managing Director of Zealous System, a leading software solutions provider.

Further studies are required to draw solid conclusions about the effectiveness and safety of chatbots. Healthcare providers can overcome this challenge by investing in data integration technologies that allow chatbots to access patient data in real-time. Implementing chatbots in healthcare requires a cultural shift, as many healthcare professionals may resist using https://chat.openai.com/ new technologies. Providers can overcome this challenge by providing staff education and training and demonstrating the benefits of chatbots in improving patient outcomes and reducing workload. Many potential benefits for the uses of chatbots within the context of health care have been theorized, such as improved patient education and treatment compliance.

• The world witnessed its first psychotherapist chatbot in 1966 when Joseph Weizenbaum created ELIZA, a natural language processing program.
• Chatbots in healthcare stand out by providing instant access to vital information, which can be crucial in emergency situations.
• Needless to say, even the smallest mistake in diagnosis can result in very serious consequences for a patient, so there is really no room for error.
• The implementation of chatbots also benefits healthcare teams by allowing them to focus on more critical tasks rather than spending excessive time managing appointment schedules manually.

Fourth, studies showed conflicting results for some outcomes (ie, anxiety and positive and negative affect). Health care providers should consider offering chatbots as an adjunct to already available interventions. Although chatbot technology for health care is continually advancing, little is known about the perspectives of practicing medical physicians on the use of chatbots in health care. It would thus seem beneficial to have medical expert opinions on the use of this technology that is intended to supplement or even replace specific roles of HCPs. The purpose of this study was to examine the perspectives of practicing medical physicians on the use of health care chatbots for patients.

By having a smart bot perform these tedious tasks, medical professionals have more time to focus on more critical issues, which ultimately results in better patient care. In conclusion, embracing the use of chatbots in healthcare holds immense promise for transforming how medical services are delivered. As technology continues to advance, these virtual assistants will play an increasingly significant role in improving patient outcomes and revolutionizing the healthcare landscape. In addition to collecting patient data and feedback, chatbots play a pivotal role in conducting automated surveys. These surveys gather valuable insights into various aspects of healthcare delivery such as service quality, satisfaction levels, and treatment outcomes.

As Nordheim et al. have pointed out, ‘the answers not only have to be correct, but they also need to adequately fulfil the users’ needs and expectations for a good answer’ (p. 25). Importantly, in addition to human-like answers, the perceived human-likeness of chatbots in general can be considered ‘as a likely predictor of users’ trust in chatbots’ (p. 25). Following Pasquale (2020), we can divide the use of algorithmic systems, such as chatbots, into two strands. First, there are those that use ML ‘to derive new knowledge from large datasets, such as improving diagnostic accuracy from scans and other images’. Second, ‘there are user-facing applications […] which interact with people in real-time’, providing advice and ‘instructions based on probabilities which the tool can derive and improve over time’ (p. 55). The latter, that is, systems such as chatbots, seem to complement and sometimes even substitute HCP patient consultations (p. 55).

Advantages of chatbots in healthcare

Moreover, healthcare chatbots are being integrated with Electronic Health Records (EHRs), enabling seamless access to patient data across various healthcare systems. This integration fosters better patient care and engagement, as medical history and patient preferences are readily available to healthcare providers, ensuring more personalized and informed care. The growing demand for virtual healthcare, accelerated by the global pandemic, has further propelled the adoption of healthcare chatbots.

In this review, the most popular databases in health and information technology were used to run the most sensitive search possible. The review minimized the risk of publication bias as much as possible through searching Google Scholar and conducting backward and forward reference list checking to identify grey literature. The search was not restricted to a certain type of chatbots, comparators, outcomes, year of publication, nor country of publication, and this makes the review more comprehensive. Even though most types of chatbots in healthcare do similar things, they have some differences we should talk about. There are many other reasons to build a healthcare chatbot, and you’ll find most of them here.

With psychiatry-oriented chatbots, people can interact with a virtual mental health ‘professional’ to get some relief. These chatbots are trained on massive data and include natural language processing capabilities to understand users’ concerns and provide appropriate advice. A chatbot can monitor available slots and manage patient meetings with doctors and nurses with a click.

They can also inform people about their list of services, the roster of specialists, and the availability of medicines, as well as provide contact information and user reviews. In the realm of post-operative care, AI chatbots help enhance overall recovery processes by using AI technology to facilitate remote monitoring of patients’ vital signs. By integrating with wearable devices or smart home technologies, these chatbots collect real-time data on metrics like heart rate, blood pressure, or glucose levels. Yes, there are mental health chatbots like Youper and Woebot, which use AI and psychological techniques to provide emotional support and therapeutic exercises, helping users manage mental health challenges.

Moreover, chatbots streamline administrative processes by automating appointment scheduling tasks, freeing up staff time for more critical responsibilities. In conclusion, healthcare chatbots have emerged as a valuable tool in the healthcare industry, revolutionizing the way patients engage with healthcare providers. Healthcare chatbots, equipped with AI, Neuro-synthetic AI, and natural language processing (NLP), are revolutionizing patient care and administrative efficiency. From setting appointment reminders and facilitating document submission to providing round-the-clock patient support, these digital assistants are enhancing the healthcare experience for both providers and patients.

Yes, implementing healthcare chatbots can lead to cost savings by automating routine administrative tasks and reducing manual labor expenses within healthcare organizations. Healthcare chatbots enhance patient engagement by providing personalized care, instant responses to queries, and convenient access to medical information anytime, anywhere. They adhere to strict data protection regulations to ensure that patient information remains confidential and secure. Moreover, chatbots simplify appointment scheduling by allowing patients to book appointments online or through messaging platforms. This not only reduces administrative overhead but also ensures that physicians’ schedules are optimized efficiently.

And finally, all information will be added to a system and will be stored in an organized and centralized manner, thus helping clinics avoid data silos and facilitate admission and tracking of patients’ conditions. After we’ve looked at the main benefits and types of healthcare chatbots, let’s move on to the most common healthcare chatbot use cases. We will also provide real-life examples to support each use case, so you have a better understanding of how exactly the bots deliver expected results.

We then discuss ethical and social issues relating to health chatbots from the perspective of professional ethics by considering professional-patient relations and the changing position of these stakeholders on health and medical assessments. Finally, to ground our analysis, we employ the perspective of HCPs and list critical aspects and challenges relating to how chatbots may transform clinical capabilities and change patient-clinician relationships in clinical practices in the long run. We stress here that our intention is not to provide empirical evidence for or against chatbots in health care; it is to advance discussions of professional ethics in the context of novel technologies. Many experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals. In this paper, we take a proactive approach and consider how the emergence of task-oriented chatbots as partially automated consulting systems can influence clinical practices and expert–client relationships.

The routine of collecting feedback can be delegated to a conversational chatbot that will listen to everything people have to tell about your organization. AI-powered conversational chatbots are typical examples of products that disrupt the contemporary healthcare industry and act as an essential element of the comprehensive digitalization drive. Chatbots, perceived as non-human and non-judgmental, provide a comfortable space for sharing sensitive medical information. Stay on this page to learn what are chatbots in healthcare, how they work, and what it takes to create a medical chatbot. There are several reasons why chatbots help healthcare organizations elevate their patient care – let’s look at each in a bit of detail. In addition to providing information, chatbots also play a vital role in contact tracing efforts.

To create a healthcare chatbot, you can use platforms like Yellow.ai, which provide tools for building AI-powered chatbots with customizable features, integration capabilities, and compliance with healthcare regulations. They send queries about patient well-being, collect feedback on treatments, and provide post-care instructions. For example, a chatbot might check on a patient’s recovery progress after surgery, reminding them of wound care practices or follow-up appointments, thereby extending the care continuum beyond the hospital.

The ability to analyze large volumes of survey responses allows healthcare organizations to identify trends, make informed decisions, and implement targeted interventions for continuous improvement. Moreover, chatbots offer an efficient way for individuals to assess their risk level without overwhelming healthcare systems already under strain due to the pandemic. Instead of inundating hospitals and clinics with patients reporting mild symptoms or seeking general advice, people can turn to chatbots for initial assessments. This reduces unnecessary burden on healthcare providers while ensuring that those who genuinely require medical attention receive it promptly.

Chatbots, also known as conversational agents, interactive agents, virtual agents, virtual humans, or virtual assistants, are computer software applications that run automated tasks or scripts designed to simulate human conversation. Chatbots are artificial intelligence (AI) programs that can generate and retrieve information for the interaction with human users via text or computer voice generation. Even with the healthcare market flooded with diverse chatbot options, there’s still a hesitancy to explore more advanced applications. This reluctance can be attributed to the nascent stage of conversational AI in healthcare, indicating that there is substantial room for growth. As advancements in natural language processing and AI continue, we can expect the emergence of more sophisticated medical assistant chatbots. In the hustle and bustle of daily life, patients may forget critical health tasks such as refilling prescriptions, adhering to medication schedules, or keeping up with vaccination timelines.

They can provide immediate responses to common queries and assist with basic tasks, but complex medical diagnoses and treatments require the expertise of trained professionals. Through conversation-based interactions, these chatbots can offer mindfulness exercises, stress management techniques, or even connect users with licensed therapists when necessary. The availability of such mental health support tools helps reduce barriers to accessing professional help while promoting emotional well-being in the medical procedure field.

Medisafe empowers users to manage their drug journey — from intricate dosing schedules to monitoring multiple measurements. Additionally, it alerts them if there’s a potential unhealthy interaction between two medications. Most chatbots (we are not talking about AI-based ones) are rather simple and their main goal is to answer common questions. Hence, when a patient starts asking about a rare condition or names symptoms that a bot was not trained to recognize, it leads to frustration on both sides. A bot doesn’t have an answer and a patient is confused and annoyed as they didn’t get help.

The key is to know your audience and what best suits them and which chatbots work for what setting. Moreover, training is essential for AI to succeed, which entails the collection of new information as new scenarios arise. However, this may involve the passing on of private data, medical or financial, to the chatbot, which stores it somewhere in the digital world.

Liji practiced as a full-time consultant in obstetrics/gynecology in a private hospital for a few years following her graduation. She has counseled hundreds of patients facing issues from pregnancy-related problems and infertility, and has been in charge of over 2,000 deliveries, striving always to achieve a normal delivery rather than operative. Also, if the chatbot has to answer a flood of questions, it may be confused and start to give garbled answers. HealthJoy’s virtual assistant, JOY, can initiate a prescription review by inquiring about a patient’s dosage, medications, and other relevant information. The Global Healthcare Chatbots Market, valued at USD 307.2 million in 2022, is projected to reach USD 1.6 billion by 2032, with a forecasted CAGR of 18.3%.

However, little is known about the perspectives of practicing medical physicians on the use of chatbots in health care, even though these individuals are the traditional benchmark of proper patient care. In today’s rapidly evolving healthcare landscape, the integration of chatbots has marked a significant leap forward. The benefits of healthcare chatbots extend across various dimensions, fundamentally reshaping patient care and operational efficiency. Chatbots play a crucial role in the collection, storage, and analysis of patient data, facilitating personalized care and treatment plans. They are designed with privacy and security measures to protect sensitive patient information, adhering to healthcare regulations. According to a research article published in the Journal of Medical Internet Research, healthcare chatbots are equipped with advanced encryption and authentication mechanisms to ensure patient data confidentiality and security.

Medical Chatbots and Sensitive Health Issues

One of the positive aspects is that healthcare organisations struggling to meet user demand for screening services can provide new patient services. However, one of the downsides is patients’ overconfidence in the ability of chatbots, which can undermine confidence in physician evaluations. COVID-19 screening is considered an ideal application for chatbots because it is a well-structured process that involves asking patients a series of clearly defined questions and determining a risk score (Dennis et al. 2020). For instance, in California, the Occupational Health Services did not have the resources to begin performing thousands of round-the-clock symptom screenings at multiple clinical sites across the state (Judson et al. 2020). To limit face-to-face meetings in health care during the pandemic, chatbots have being used as a conversational interface to answer questions, recommend care options, check symptoms and complete tasks such as booking appointments. 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.

Second, the full texts of studies included from the first step were read independently by the same reviewers. Any disagreements between the reviewers were resolved by discussion or by consulting a third reviewer (MH). Cohen κ [21] was calculated to assess interrater agreement between reviewers, which was 0.85 and 0.89 in the first and second step of the selection process, respectively, indicating a very good level of agreement [22]. 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. 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.

Further, search terms related to mental disorders were derived from the Medical Subject Headings index in MEDLINE. The search strings utilized for searching each bibliographic database are shown in Multimedia Appendix 2. Relevant is ready to consult you and help you create an informational, administrative, hybrid chatbot, etc. Skillful in healthcare software development, our dedicated developers can utilize out-of-the-box components or create custom medical сonversational AI chatbots from the ground up. No matter what kind of healthcare area you are in – telehealth, mental support, or insurance processing, we will bring you invaluable benefits in saving costs, automating business processes, and giving you a great opportunity to maintain profits.

One of the key advantages of using chatbots for scheduling appointments is their ability to integrate with existing systems. These intelligent bots can instantly check doctors’ availability in real-time before confirming appointments. This integration ensures that patients are promptly assigned to an available doctor without any delays or confusion. Gone are the days of endless phone calls and waiting on hold while staff members manually check schedules. In the domain of mental health, chatbots like Woebot use CBT techniques to offer emotional support and mental health exercises.

Secondly, placing too much trust in chatbots may potentially expose the user to data hacking. And finally, patients may feel alienated from their primary care physician or self-diagnose once too often. Chatbots can be exploited to automate some aspects of clinical decision-making by developing protocols based on data analysis.

Top 3 Healthcare Chatbots

Also known as informative, these bots are here to answer questions, provide requested information, and guide you through services of a healthcare provider. If such a bot is AI-powered, it can also adapt to a conversation, become proactive instead of reactive, and overall understand the sentiment. But even if the conversational bot does not have an innovative technology in its backpack, it can still be a highly valuable tool for quickly offering the needed information to a user. One of the rising trends in healthcare is precision medicine, which implies the use of big data to provide better and more personalized care. To obtain big data, healthcare organizations need to use multiple data sources, and healthcare chatbots are actually one of them. A distinctive feature of a chatbot technology in healthcare is its ability to immediately respond to a request, and this is another big benefit.

The study experimentally tested the impact of different scenarios involving experiences of embarrassing and stigmatizing health conditions on participant preferences for medical consultations. Medical chatbots provide quick and convenient health information by tapping into an ever-expanding array of databases and sources of knowledge. According to a scoping review conducted by Abd-alrazaq et al [13], chatbots are used for many mental disorders, such as autism, post-traumatic stress disorder, substance use disorders, schizophrenia, and dementia. The current review did not find any study assessing the effectiveness or safety of chatbots used for these disorders. This highlights a pressing need to examine the effectiveness and safety of chatbots targeting patients with autism, post-traumatic stress disorder, substance use disorders, schizophrenia, and dementia. This review showed that there is a lack of evidence assessing the effectiveness and safety of chatbots.

The questions can be pre-built in the dialogue window, so the user only has to choose the needed one. Despite its simplicity, the FAQ bot is helpful as it can speed up the process of getting the patient to the right specialist or at least provide them with basic answers. Such fast processing of requests also adds to overall patient satisfaction and saves both doctors’ and patients’ time.

In traditional patient care, a patient might have to wait for quite some time to get an answer to their question. With smart chatbots, not only the patient receives a reply within seconds, but exactly when the information is needed the most. And one more great thing about chatbots is that one bot can process multiple Chat PG requests simultaneously, while a doctor cannot do so. AI Chatbots also play a crucial role in the healthcare industry by offering mental health support. They provide resources and guide users through coping strategies, creating a safe space for individuals to discuss their emotional well-being anonymously.

In September 2020, the THL released the mobile contact tracing app Koronavilkku,1 which can collaborate with Omaolo by sharing information and informing the app of positive test cases (THL 2020, p. 14). Healthcare chatbots are not only reasonable solutions for your patients but your doctors as well. Imagine how many more patients you can connect with if you save time and effort by automating responses to repetitive questions of patients and basic activities like appointment scheduling or providing health facts. Chatbot solution for healthcare industry is a program or application designed to interact with users, particularly patients, within the context of healthcare services. They can be powered by AI (artificial intelligence) and NLP (natural language processing). While chatbots are valuable tools in healthcare, they cannot replace human doctors entirely.

You can foun additiona information about ai customer service and artificial intelligence and NLP. In the early days, the problem of these systems was ‘the complexity of mapping out the data in’ the system (Fischer and Lam 2016, p. 23). Today, advanced AI technologies and various kinds of platforms that house big data (e.g. blockchains) are able to map out and compute in real time most complex data structures. In addition, especially in health care, these systems have been based on theoretical and practical models and methods developed in the field. For example, in the field of psychology, so-called ‘script theory’ provided a formal framework for knowledge (Fischer and Lam 2016).

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. Based on the user’s intent, the chatbot retrieves relevant information from its database or interacts with external systems like electronic health records. The information is then processed and tailored into a response that addresses the user’s needs.

Thus, chatbot platforms seek to automate some aspects of professional decision-making by systematising the traditional analytics of decision-making techniques (Snow 2019). In the long run, algorithmic solutions are expected to optimise the work tasks of medical doctors in terms of diagnostics and replace the routine tasks of nurses through online consultations benefits of chatbots in healthcare and digital assistance. In addition, the development of algorithmic systems for health services requires a great deal of human resources, for instance, experts of data analytics whose work also needs to be publicly funded. A complete system also requires a ‘back-up system’ or practices that imply increased costs and the emergence of new problems.

It not only improves patient access to immediate health advice but also helps streamline emergency room visits by filtering non-critical cases. For instance, chatbots can engage patients in their treatment plans, provide educational content, and encourage lifestyle changes, leading to better health outcomes. This interactive model fosters a deeper connection between patients and healthcare services, making patients feel more involved and valued. Medical chatbots respond to prompts and data shared by users about their health to offer relevant information, guidance, and advice. As healthcare systems grapple with staffing shortages and overburdened resources, medical chatbots could offer a digital lifeline. The current review identified heterogeneity in the tools used to measure the same outcomes and in the research design.

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. Healthcare payers and providers, including medical assistants, are also beginning to leverage these AI-enabled tools to simplify patient care and cut unnecessary costs. 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. Many healthcare experts feel that chatbots may help with the self-diagnosis of minor illnesses, but the technology is not advanced enough to replace visits with medical professionals.

The ability of chatbots to cater to a broader audience underscores their potential in making healthcare services more accessible, thus bridging the gap between medical professionals and patients. This increased accessibility is crucial for extending medical assistance to larger populations, democratizing the availability of health information and support. Beyond administrative support, chatbots in healthcare extend their utility to patient monitoring and care. They offer personalized informational support, field health-related questions, and ensure patients adhere to their medication schedules, which plays a pivotal role in improving health outcomes.

The most famous chatbots currently in use are Siri, Alexa, Google Assistant, Cordana and XiaoIce. Two of the most popular chatbots used in health care are the mental health assistant Woebot and Omaolo, which is used in Finland. From the emergence of the first chatbot, ELIZA, developed by Joseph Weizenbaum (1966), chatbots have been trying to ‘mimic human behaviour in a text-based conversation’ (Shum et al. 2018, p. 10; Abd-Alrazaq et al. 2020). Thus, their key feature is language and speech recognition, that is, natural language processing (NLP), which enables them to understand, to a certain extent, the language of the user (Gentner et al. 2020, p. 2). Despite the initial chatbot hype dwindling down, medical chatbots still have the potential to improve the healthcare industry.

These AI-driven platforms have become essential tools in the digital healthcare ecosystem, enabling patients to access a range of healthcare services online from the comfort of their homes. Healthcare chatbots are AI-powered virtual assistants that provide personalized support to patients and healthcare providers. They are designed to simulate human-like conversation, enabling patients to interact with them as they would with a real person.

A chatbot can be defined as specialized software that is integrated with other systems and hence, it operates in a digital environment. This means, chatbots and the data that they process might be exposed to threat agents and might be a target for cyberattacks. When a patient with a serious condition addresses a medical professional, they often need advice and reassurance, which only a human can give. Thus, a chatbot may work great for assistance with less major issues like flu, while a real person can remain solely responsible for treating patients with long-term, serious conditions. In addition, there should always be an option to connect with a real person via a chatbot, if needed.

• This research was internally funded and received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
• With standalone chatbots, businesses have been able to drive their customer support experiences, but it has been marred with flaws, quite expectedly.
• This breaks down the user input for the chatbot to understand the user’s intent and context.
• Healthcare chatbots have been instrumental in addressing public health concerns, especially during the COVID-19 pandemic.
• 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.
• They are AI-powered virtual assistants designed to automate routine administrative tasks, streamline workflows, and improve operational efficiency across healthcare facilities.

This trend highlights the healthcare industry’s recognition of chatbots as a pivotal tool in enhancing patient experiences, facilitating healthcare automation, and Improving patient experience with chatbots. 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. Physicians’ autonomy to diagnose diseases is no end in itself, but patients’ trust in a chatbot about the nature of their disease can impair professionals in their ability to provide appropriate care for patients if they disregard a doctor’s view.

The primary role of healthcare chatbots is to streamline communication between patients and healthcare providers. They serve as round-the-clock digital assistants, capable of handling a wide array of tasks – from answering common health queries and scheduling appointments to reminding patients about medication and providing tailored health advice. This constant availability not only enhances patient engagement but also significantly reduces the workload on healthcare professionals.