Medical Chatbots Use Cases, Examples and Case Studies of Generative Conversational AI in Medicine and Health

As such models are formal (and have already been accepted and in use), it is relatively easy to turn them into algorithmic form. The rationality in the case of models and algorithms is instrumental, and one can say that an algorithm is ‘the conceptual embodiment of instrumental rationality within’ (Goffey 2008, p. 19) machines. Thus, algorithms are an actualisation of reason in the digital domain (e.g. Finn 2017; Golumbia 2009).

• 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.
• 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.
• According to Forbes, the number of people with anxiety disorders grew from 298 million to 374 million, which is really a significant increase.
• The CancerChatbot by CSource is an artificial intelligence healthcare chatbot system for serving info on cancer, cancer treatments, prognosis, and related topics.
• These companies majorly use healthcare chatbots to provide potential patients with proper access to healthcare information and help them find appropriate healthcare treatments in case of medical emergencies.

The chatbots are text or voice-based conversational software that connects users to the service provider to execute certain actions/query and to retrieve necessary information through mobile applications, websites, or telephone. The chatbots are artificial intelligence (AI) programs and can simulate a conversation with a user in natural language. The chatbots can process different types of data such as texts, images and videos. With psychiatric disorders affecting at least 35% of patients with cancer, comprehensive cancer care now includes psychosocial support to reduce distress and foster a better quality of life [80]. The first chatbot was designed for individuals with psychological issues [9]; however, they continue to be used for emotional support and psychiatric counseling with their ability to express sympathy and empathy [81]. 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].

Professional development

While clinicians can enhance patient care through unified hospital communication and centralized storage of patient data. Let’s take a moment to look at the areas of healthcare where custom medical chatbots have proved their worth. As patients continuously receive quick and convenient access to medical services, their trust in the chatbot technology will naturally grow. 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.

You do not design a conversational pathway the way you perceive your intended users, but with real customer data that shows how they want their conversations to be. This chatbot solution for healthcare helps patients get all the details they need about a cancer-related topic in one place. It also assists healthcare providers by serving info to cancer patients and their families. This free AI-enabled chatbot allows you to input your symptoms and get the most likely diagnoses.

Healthcare Providers

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.

The advantages of chatbots in healthcare are enormous – and all stakeholders share the benefits. Neither does she miss a dose of the prescribed antibiotic – a healthcare chatbot app brings her up to speed on those details. Input modality, or how the user interacts with the chatbot, was primarily text-based (96%), with seven apps (9%) allowing for spoken/verbal input, and three (4%) allowing for visual input.

Conversational Chatbots

The chatbots can help them by listening to their concerns and providing necessary answers or solutions. If the conversation becomes something the chatbots cannot handle, it will be transferred to a human expert, who can refer to the previous conversations to help the patient. The data stored in the chatbots can be analyzed and used to predict the future trends of patients’ health and mental wellness and the evolution of chatbots in the healthcare industry. Chatbots provide immediate access to medical information and assistance, allowing patients to seek advice or schedule appointments at any time. Chatbots have emerged as valuable tools in the healthcare sector, offering a myriad of benefits that enhance patient care and streamline operational processes.

The chatbots can make recommendations for care options once the users enter their symptoms. Chatbots called virtual assistants or virtual humans can handle the initial contact with patients, asking and answering the routine questions that inevitably come up. During the coronavirus disease 2019 (COVID-19) pandemic, especially, screening for this infection by asking certain questions in a certain predefined order, and thus assessing the risk of COVID-19 could save thousands of manual screenings. This app is very adamant in stating that it doesn’t provide diagnosis or treatment.

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.

• Hence, every healthcare services provider needs to think about ways of strengthening their digital environment, including chatbots.
• Google has also expanded this opportunity for tech companies to allow them to use its open-source framework to develop AI chatbots.
• Since chatbots used for patient care require access to multiple data sets, it is mandatory for AI-based tools such as chatbots to adhere to all data security protocols implemented by government and regulatory authorities.
• 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.
• Similarly, Sense.ly (Sense.ly, Inc) acts as a web-based nurse to assist in monitoring appointments, managing patients’ conditions, and suggesting therapies.
• One stream of healthcare chatbot development focuses on deriving new knowledge from large datasets, such as scans.

Molly, a virtual nurse bot, assists patients in maintaining treatment plans and records. The everyday data and vital parameters of the patients are also stored, and the physician is notified if there are any health-related risks to the patient. Healthcare chatbots were earlier considered to be a fad that would fade away in a few years, but their applications are now widely increased, with every company leveraging its power to address its pain points.

XZEVN: mental health chatbot

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. Chatbot technology is still in its infancy when it comes to the healthcare sector. Maybe through research and experiments, healthcare chatbots can come into the limelight.

The number of studies assessing the development, implementation, and effectiveness are still relatively limited compared with the diversity of chatbots currently available. Further studies are required to establish the efficacy across various conditions and populations. Nonetheless, chatbots for self-diagnosis are an effective way of advising patients as the first point of contact if accuracy and sensitivity requirements can be satisfied. Among all the emerging technologies in the healthcare industry, the chatbots (or virtual assistants) are gaining significant attention among the healthcare provider and patients.

HealthTap on Draft

Also, approximately 89% of healthcare organizations state that they experienced an average of 43 cyberattacks per year, which is almost one attack every week. Chatbots in healthcare industry are awesome – but as any other great technology, they come with several concerns and limitations. It is important to know about them before implementing the technology, so in the future you will face little to no issues. First, chatbots provide a high level of personalization due to the analysis of patient’s data. In this way, a bot suggests relevant recommendations and guidance and receive advice, tailored specifically to their needs and/or condition. By optimizing workflows, improving patient engagement, and enhancing overall operational efficiency, Chat360 empowers businesses in the healthcare industry to deliver a superior and technologically advanced healthcare experience.

Thus, new technologies require system-level assessment of their effects in the design and implementation phase. Since the 1950s, there have been efforts aimed at building models and systematising physician decision-making. For example, in the field of psychology, the so-called framework of ‘script theory’ was ‘used to explain how a physician’s medical diagnostic knowledge is structured for diagnostic problem solving’ (Fischer and Lam 2016, p. 24). According to this theory, ‘the medical expert has an integrated network of prior knowledge that leads to an expected outcome’ (p. 24).

The American Society of Clinical Oncology (ASCO) is one of the largest and most respected conferences in the field of oncology. Held annually, this conference brings together researchers, physicians, and other healthcare professionals from around the world to discuss the latest advances in cancer research, diagnosis, and treatment. In today’s highly competitive and advancing chatbots in healthcare industry healthcare environment chatbots are playing a crucial role in the healthcare delivery system. Like for other businesses, customer experience and operational efficiency are the two most crucial aspects for healthcare also. Chatbots facilitate an efficient and effective way of self-service delivery system to the patient irrespective of time and his location.

Elomia is an artificial intelligence-powered virtual therapist who lives in your phone. A study found that 89% of patients Google their health symptoms before scheduling an appointment. You can’t blame people for doing this, not knowing what’s wrong with you the moment you feel unwell is nerve-wracking. Chatbot becomes a vital point of communication and information gathering at unforeseeable times like a pandemic as it limits human interaction while still retaining patient engagement. Hence, it’s very likely to persist and prosper in the future of the healthcare industry. Now that we understand the myriad advantages of incorporating chatbots in the healthcare sector, let us dive into what all kinds of tasks a chatbot can achieve and which chatbot abilities resonate best with your business needs.

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. Although there are a variety of techniques for the development of chatbots, the general layout is relatively straightforward.

Chatbot apps were downloaded globally, including in several African and Asian countries with more limited smartphone penetration. 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. When you are ready to invest in conversational AI, you can identify the top vendors using our data-rich vendor list on voice AI or chatbot platforms. By using SalesIQ specifically, patients can initiate conversation in an all-in-one live chatbot platform.