AI Chatbots in Healthcare Examples + Development Guide

Chatbots assist doctors by automating routine tasks, such as appointment scheduling and patient inquiries, freeing up their time for more complex medical cases. They also provide doctors with quick access to patient data and history, enabling more informed and efficient decision-making. Patients can interact with the chatbot to find the most convenient appointment times, thus reducing the administrative burden on hospital staff.

For instance, a healthcare chatbot uses AI to evaluate symptoms against a vast medical database, providing patients with potential diagnoses and advice on the next steps. It not only improves patient access to immediate health advice but also helps streamline emergency room visits by filtering non-critical cases. In the near future, healthcare chatbots are expected to evolve into sophisticated companions for patients, offering real-time health monitoring and automatic aid during emergencies. Their capability to continuously track health status and promptly respond to critical situations will be a game-changer, especially for patients managing chronic illnesses or those in need of constant care.

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

Therefore, you should choose the right chatbot for the use cases that you will need it for. The virtual assistant also gives you the option to authenticate signatures in real time. The bot performs banking activities, such as checking balance, funds transfers, and bill payments. It can also provide information about spending trends and credit scores for a full account analysis view. Bots can also monitor the user’s emotional health with personalized conversations using a variety of psychological techniques.

Similarly, Sense.ly (Sense.ly, Inc) acts as a web-based nurse to assist in monitoring appointments, managing patients’ conditions, and suggesting therapies. Another chatbot that reduces the burden on clinicians and decreases wait time is Careskore (CareShore, Inc), which tracks vitals and anticipates the need for hospital admissions [42]. Chatbots have also been proposed to autonomize patient encounters through several advanced eHealth services. In addition to collecting data and providing bookings, Health OnLine Medical Suggestions or HOLMES (Wipro, Inc) interacts with patients to support diagnosis, choose the proper treatment pathway, and provide prevention check-ups [44].

Provide mental health assistance

Today, chatbots offer diagnosis of symptoms, mental healthcare consultation, nutrition facts and tracking, and more. For example, in 2020 WhatsApp collaborated with the World Health Organization (WHO) to make a chatbot service that answers users’ questions on COVID-19. However, some of these were sketches of the interface rather than the final user interface, and most of the screenshots had insufficient description as to what the capabilities were. Although the technical descriptions of chatbots might constitute separate papers in their own right, these descriptions were outside the scope for our focus on evidence in public health. A further scoping study would be useful in updating the distribution of the technical strategies being used for COVID-19–related chatbots.

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). Healthcare chatbots are AI-enabled digital assistants that allow patients to assess their health chatbot healthcare use cases 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. They provide preliminary assessments, answer general health queries, and facilitate virtual consultations.

Schedule Appointments and Set Reminders

Further, as a chatbot could belong to multiple categories (e.g., delivered multiple use cases), our numbers do not always add up to 61. Research on the use of chatbots in public health service provision is at an early stage. Although preliminary results do indicate positive effects in a number of application domains, reported findings are for the most part mixed. For RCTs, the number of participants varied between 20 to 927, whereas user analytics studies considered data from between 129 and 36,070 users. Overall, the evidence found was positive, showing some beneficial effect, or mixed, showing little or no effect.

Customer feedback surveys is another healthcare chatbot use case where the bot collects feedback from the patient post a conversation. It can be via a CSAT rating or a detailed rating system where patients can rate their experience for different types of services. Since the bot records the appointments for all patients, it can also be programmed to send reminder notifications and things to carry before the appointment. It eliminates the need for hospital administrators to do the same manually over a call. This healthcare chatbot use case is reliable because it reduces errors and is intuitive since the user gets a quick overview of the available spots. Studies show that chatbots in healthcare are expected to grow at an exponential rate of 19.16% from 2022 to 2030.

The future perspective of chatbots for healthcare

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 global healthcare chatbots market accounted for $116.9 million in 2018 and is expected to reach a whopping $345.3 million by 2026, registering a CAGR of 14.5% from 2019 to 2026. 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. Furthermore, there are work-related and ethical standards in different fields, which have been developed through centuries or longer. For example, as Pasquale argued (2020, p. 57), in medical fields, science has made medicine and practices more reliable, and ‘medical boards developed standards to protect patients from quacks and charlatans’. Thus, one should be cautious when providing and marketing applications such as chatbots to patients.

It features many tools, such as online doctor consultations, appointment settings, and, most importantly, a symptom checker. It costs $14.99/month for the Pro version, which provides unlimited conversations with chatbots, personalized health reports, and grants you early access to new features. Medical chatbots might pose concerns about the privacy and security of sensitive patient data. Because the last time you had the flu and searched your symptoms on Google, it made you paranoid.

Another app is Weight Mentor, which provides self-help motivation for weight loss maintenance and allows for open conversation without being affected by emotions [47]. Health Hero (Health Hero, Inc), Tasteful Bot (Facebook, Inc), Forksy (Facebook, Inc), and SLOWbot (iaso heath, Inc) guide users to make informed decisions on food choices to change unhealthy eating habits [48,49]. The effectiveness of these apps cannot be concluded, as a more rigorous analysis of the development, evaluation, and implementation is required.

AI Powered Chatbot Use Cases in Healthcare

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). 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. As they interact with patients, they collect valuable health data, which can be analyzed to identify trends, optimize treatment plans, and even predict health risks. This continuous collection and analysis of data ensure that healthcare providers stay informed and make evidence-based decisions, leading to better patient care and outcomes.

The development—especially conceptual in nature—of ADM has one of its key moments in the aftermath of World War II, that is, the era of the Cold War. America and the Soviets were both keen (in their own ways) on find ways to automatise and streamline their societies (including decision-making). In the field of medical practice, probability assessments has been a recurring theme. 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.