AI Chatbots in Healthcare Examples + Development Guide

Chatbots are computer programs that present a conversation-like interface through which people can access information and services. The COVID-19 pandemic has driven a substantial increase in the use of chatbots to support and complement traditional health care systems. However, despite the uptake in their use, evidence to support the development and deployment of chatbots in public health remains limited. Recent reviews have focused on the use of chatbots during the COVID-19 pandemic and the use of conversational agents in health care more generally.

This area holds tremendous potential, as an estimated ≥50% of all patients with cancer have used radiotherapy during the course of their treatment. Early cancer detection can lead to higher survival rates and improved quality of life. Inherited factors are present in 5% to 10% of cancers, including breast, colorectal, prostate, and rare tumor syndromes [62]. use of chatbots in healthcare Family history collection is a proven way of easily accessing the genetic disposition of developing cancer to inform risk-stratified decision-making, clinical decisions, and cancer prevention [63]. The web-based chatbot ItRuns (ItRunsInMyFamily) gathers family history information at the population level to determine the risk of hereditary cancer [29].

The Rasa Core is the chatbot framework that predicts the next best action using a deep learning model. Rasa NLU is an open-source library for natural language understanding used for intent classification, response generation and retrieval, entity extraction in designing chatbot conversations. Rasa’s NLU component used to be separate but merged with Rasa Core into a single framework.

For example, when a physician prescribes medication, a chatbot can automatically send an electronic prescription directly to pharmacies, eliminating the need for manual intervention. 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 addition to educating patients, AI chatbots also play a crucial role in promoting preventive care.

For example, ChatGPT 4 and ChatGPT 3.5 LLMs are deployed on cloud servers that are located in the US. Hence, per the GDPR law, AI chatbots in the healthcare industry that https://chat.openai.com/ use these LLMs are forbidden from being used in the EU. Patients can use text, microphones, or cameras to get mental health assistance to engage with a clinical chatbot.

The Ethics of Using Chatbots in Healthcare

If you want your company to benefit financially from AI solutions, knowing the main chatbot use cases in healthcare is the key. With abundant benefits and rapid innovation in conversational AI, adoption is accelerating quickly. It also increases revenue as the reduction in the consultation periods and hospital waiting lines leads healthcare institutions to take in and manage more patients. Physicians worry about how their patients might look up and try cures mentioned on dubious online sites, but with a chatbot, patients have a dependable source to turn to at any time. As well, virtual nurses can send daily reminders about the medicine intake, ask patients about their overall well-being, and add new information to the patient’s card. In this way, a patient does not need to directly contact a doctor for an advice and gains more control over their treatment and well-being.

AI Chatbots have revolutionized the way patient data is collected in healthcare settings. With their efficient capabilities, they streamline the process of gathering vital information during initial assessments or follow-up consultations. By engaging patients in interactive conversations, chatbots can elicit detailed responses and ensure accurate data collection. The use of chatbots in healthcare has become increasingly prevalent, particularly in addressing public health concerns, including COVID-19 pandemic during previous years.

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. Babylon Health offers AI-driven consultations with a virtual doctor, a patient chatbot, and a real doctor. Chatbot developers should employ a variety of chatbots to engage and provide value to their audience. The key is to know your audience and what best suits them and which chatbots work for what setting. Healthcare chatbot diagnoses rely on artificial intelligence algorithms that continuously learn from vast amounts of data. All authors contributed to the assessment of the apps, and to writing of the manuscript.

The Health Insurance and Portability and Accountability Act (HIPAA) of 1996 is United States regulation that sets the standards for using, handling, and storing sensitive healthcare data. That sums up our module on training a conversational model for classifying intent and extracting entities using Rasa NLU. Your next step is to train your chatbot to respond to stories in a dialogue platform using Rasa core.

Addressing Public Health Concerns like the COVID-19 Symptom Checker

Where there is evidence, it is usually mixed or promising, but there is substantial variability in the effectiveness of the chatbots. This finding may in part be due to the large variability in chatbot design (such as differences in content, features, and appearance) but also the large variability in the users’ response to engaging with a chatbot. Although studies have shown that AI technologies make fewer mistakes than humans in terms of diagnosis and decision-making, they still bear inherent risks for medical errors [104]. The interpretation of speech remains prone to errors because of the complexity of background information, accuracy of linguistic unit segmentation, variability in acoustic channels, and linguistic ambiguity with homophones or semantic expressions. 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].

A medical bot can recognize when a patient needs urgent help if trained and designed correctly. It can provide immediate attention from a doctor by setting appointments, especially during emergencies. 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. A thorough research of LLMs is recommended to avoid possible technical issues or lawsuits when implementing a new artificial intelligence chatbot.

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.

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. Service-provided classification is dependent on sentimental proximity to the user and the amount of intimate interaction dependent on the task performed. This can be further divided into interpersonal for providing services to transmit information, intrapersonal for companionship or personal support to humans, and interagent to communicate with other chatbots [14]. The next classification is based on goals with the aim of achievement, subdivided into informative, conversational, and task based. Response generation chatbots, further classified as rule based, retrieval based, and generative, account for the process of analyzing inputs and generating responses [16].

It can be done via different ways, by asking questions or through a questionnaire that a patient fills in themselves. In this way, a patient learns about their condition and its severity and the bot, in return, suggests a treatment plan or even notifies the doctor in case of an emergency. This bot is similar to a conversational one but is much simpler as its main goal is to provide answers to frequently asked questions. 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.

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. Healthbots are computer programs that mimic conversation with users using text or spoken language9. The advent of such technology has created a novel way to improve person-centered healthcare. The underlying technology that supports such healthbots may include a set of rule-based algorithms, or employ machine learning techniques such as natural language processing (NLP) to automate some portions of the conversation.

Chatbots are now able to provide patients with treatment and medication information after diagnosis without having to directly contact a physician. Such a system was proposed by Mathew et al [30] that identifies the symptoms, predicts the disease using a symptom–disease data set, and recommends a suitable treatment. Although this may seem as an attractive option for patients looking for a fast solution, computers are still prone to errors, and bypassing professional inspection may be an area of concern. Chatbots may also be an effective resource for patients who want to learn why a certain treatment is necessary. Madhu et al [31] proposed an interactive chatbot app that provides a list of available treatments for various diseases, including cancer.

By providing remote assistance through chat interfaces, healthcare organizations can optimize their resources and prioritize urgent cases effectively. 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. 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.

With a comprehensive understanding of IT processes, I am able to identify and effectively address the diverse needs of firms and industries. You’ll need to define the user journey, planning ahead for the patient and the clinician side, as doctors will probably need to make decisions based on the extracted data. These chatbots are equipped with the simplest AI algorithms designed to distribute information via pre-set responses.

Unfortunately, the healthcare industry experiences a rise of attacks, if compared to past years. For example, there was an increase of 84% in healthcare breaches, comparing the numbers from 2018 to 2021. Also, approximately 89% of healthcare organizations state that they experienced an average of 43 cyberattacks per year, which is almost one attack every week. 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.

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. Forksy is the go-to digital nutritionist that helps you track your eating habits by giving recommendations about diet and caloric intake. This chatbot tracks your diet and provides automated feedback to improve your diet choices; plus, it offers useful information about every food you eat – including the number of calories it contains, and its benefits and risks to health. 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.

A study performed on Woebot, developed based on cognitive behavioral therapy, showed that depressive symptoms were significantly reduced, and participants were more receptive than in traditional therapies [41]. This agreed with the Shim results, also using the same type of therapy, which showed that the intervention was highly engaging, improved well-being, and reduced stress [82]. When another chatbot was developed based on the structured association technique counseling method, the user’s motivation was enhanced, and stress was reduced [83].

And there are many more chatbots in medicine developed today to transform patient care. By leveraging chatbot technology for survey administration, hospitals and clinics can achieve higher response rates compared to traditional methods like paper-based surveys or phone interviews. You can foun additiona information about ai customer service and artificial intelligence and NLP. Patients find it convenient to provide feedback through user-friendly interfaces at their own pace without any external pressure. Use case for chatbots in oncology, with examples of current specific applications or proposed designs. Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.

Patients can receive immediate assistance on a wide range of topics such as medication information or general health advice. This not only saves time but also reduces unnecessary visits to healthcare facilities. AI Chatbots have revolutionized the healthcare industry, offering a wide range of benefits that enhance accessibility, improve patient engagement, and reduce costs.

Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. First, we used IAB categories, classification parameters utilized by 42Matters; this relied on the correct classification of apps by 42Matters Chat PG and might have resulted in the potential exclusion of relevant apps. Additionally, the use of healthbots in healthcare is a nascent field, and there is a limited amount of literature to compare our results. Furthermore, we were unable to extract data regarding the number of app downloads for the Apple iOS store, only the number of ratings. This resulted in the drawback of not being able to fully understand the geographic distribution of healthbots across both stores.

Once again, go back to the roots and think of your target audience in the context of their needs. Hospitals can use chatbots for follow-up interactions, ensuring adherence to treatment plans and minimizing readmissions. Capacity is an AI-powered support automation platform that provides an all-in-one solution for automating support and business processes. It connects your entire tech stack to answer questions, automate repetitive support tasks, and build solutions to any business challenge. 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.

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. Predetermined responses are then generated by analyzing user input, on text or spoken ground, and accessing relevant knowledge [3]. Problems arise when dealing with more complex situations in dynamic environments and managing social conversational practices according to specific contexts and unique communication strategies [4].

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. First, the chatbot helps Peter relieve the pressure of his perceived mistake by letting him know it’s not out of the ordinary, which may restore his confidence; then, it provides useful steps to help him deal with it better. This allows doctors to process prescription refills in batch or automate them in cases where doctor intervention is not necessary. If you’re enjoying this article, consider supporting our award-winning journalism by subscribing.

Chatbots were found to have improved medical service provision by reducing screening times [17] and triaging people with COVID-19 symptoms to direct them toward testing if required. These studies clearly indicate that chatbots were an effective tool for coping with the large numbers of people in the early stages of the COVID-19 pandemic. Overall, this result suggests that although chatbots can achieve useful scalability properties (handling many cases), accuracy is of active concern, and their deployment needs to be evidence-based [23]. Surprisingly, there is no obvious correlation between application domains, chatbot purpose, and mode of communication (see Multimedia Appendix 2 [6,8,9,16-18,20-45]). Some studies did indicate that the use of natural language was not a necessity for a positive conversational user experience, especially for symptom-checking agents that are deployed to automate form filling [8,46]. In another study, however, not being able to converse naturally was seen as a negative aspect of interacting with a chatbot [20].

• Many of the apps reviewed were focused on mental health, as was seen in other reviews of health chatbots9,27,30,33.
• To obtain big data, healthcare organizations need to use multiple data sources, and healthcare chatbots are actually one of them.
• This was made possible through deep learning algorithms in combination with the increasing availability of databases for the tasks of detection, segmentation, and classification [57].

Finally, the issue of fairness arises with algorithm bias when data used to train and test chatbots do not accurately reflect the people they represent [101]. 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. Identifying the source of algorithm bias is crucial for addressing health care disparities between various demographic groups and improving data collection.

A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. There were 47 (31%) apps that were developed for a primary care domain area and 22 (14%) for a mental health domain. Involvement in the primary care domain was defined as healthbots containing symptom assessment, primary prevention, and other health-promoting measures.

As we mentioned earlier, the collection of information is vital for the healthcare sector as it allows more personalized healthcare and, as a result, leads to more satisfied patients. Hence, these bots are really important as they help healthcare organizations evaluate their services, understand their patients better, and overall gain a better understanding of what might be improved and how. As the name implies, prescriptive chatbots are used to provide a therapeutic solution to a patient by learning about their needs and symptoms through a conversation. Such chatbot for medical diagnosis usually asks questions and encourages patients to share their symptoms in order to understand their current condition and what kind of treatment is recommended. Note though that a prescriptive chatbot cannot replace a doctor, and medical consultation is still needed.

After training your chatbot on this data, you may choose to create and run a nlu server on Rasa. The first step is to set up the virtual environment for your chatbot; and for this, you need to install a python module. All these platforms, except for Slack, provide a Quick Reply as a suggested action that disappears once clicked. Users choose quick replies to ask for a location, address, email, or simply to end the conversation. This concept is described by Paul Grice in his maxim of quantity, which depicts that a speaker gives the listener only the required information, in small amounts. One of the key elements of an effective conversation is turn-taking, and many bots fail in this aspect.

This virtual assistant is available at any time to address medical concerns and offer personalized guidance, making it easier for patients to have conversations with hospital staff and pharmacies. The convenience and accessibility of chatbots have transformed the physician-patient relationship. Regulatory standards have been developed to accommodate for rapid modifications and ensure the safety and effectiveness of AI technology, including chatbots. With the growing number of AI algorithms approved by the Food and Drug Administration, they opened public consultations for setting performance targets, monitoring performance, and reviewing when performance strays from preset parameters [102]. The American Medical Association has also adopted the Augmented Intelligence in Health Care policy for the appropriate integration of AI into health care by emphasizing the design approach and enhancement of human intelligence [109].

However, the field of chatbot research is in its infancy, and the evidence for the efficacy of chatbots for prevention and intervention across all domains is at present limited. For each language, the team checked whether the chatbots answered questions correctly, comprehensively and appropriately—qualities that would be expected of a human expert’s answer. The study authors used an AI tool (GPT-3.5) to compare generated responses against the answers provided in the three medical datasets. Finally, human assessors double-checked a portion of those evaluations to confirm the AI judge was accurate. Thirunavukarasu, though, says he wonders about the extent to which artificial intelligence and human evaluators agree; people can, after all, disagree over critiques of comprehension and other subjective traits. Additional human study of the generated answers would help clarify conclusions about chatbots’ medical usefulness, he adds.

Personalization features were only identified in 47 apps (60%), of which all required information drawn from users’ active participation. Forty-three of these (90%) apps personalized the content, and five (10%) personalized the user interface of the app. Examples of individuated content include the healthbot asking for the user’s name and addressing them by their name; or the healthbot asking for the user’s health condition and providing information pertinent to their health status.

And due to a fact that the bot is basically a robot, all these actions take little time and the appointment can be scheduled within minutes. In this way, a patient can conveniently schedule an appointment at any time and from anywhere (most importantly, from the comfort of their own home) while a doctor will simply receive a notification and an entry in their calendar. As a Business Analyst with 4+ years of experience at Acropolium, I have served as a vital link between our software development team and clients.

Additionally, focus areas including anesthesiology, cancer, cardiology, dermatology, endocrinology, genetics, medical claims, neurology, nutrition, pathology, and sexual health were assessed. As apps could fall within one or both of the major domains and/or be included in multiple focus areas, each individual domain and focus area was assigned a numerical value. 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.