Top 10 Chatbots in Healthcare: Insights & Use Cases in 2023

A chatbot can offer a safe space to patients and interact in a positive, unbiased language in mental health cases. Mental health chatbots like Woebot, Wysa, and Youper are trained in Cognitive Behavioural Therapy (CBT), which helps to treat problems by transforming the way patients think and behave. Over the past few years, artificial intelligence (AI) has made significant advancements in the healthcare industry. One of the most prominent AI-powered tools is ChatGPT, a natural language processing model developed by OpenAI.

Chatbots ask patients about their current health issue, find matching physicians and dentists, provide available time slots, and can schedule, reschedule, and delete appointments for patients. Chatbots can also be integrated into user’s device calendars to send reminders and updates about medical appointments. Crucially, the report’s authors said that the higher numbers of patients being referred for help from the services did not increase waiting times or cause a reduction in the number of clinical assessments being performed. That’s because the detailed information the chatbot collected reduced the amount of time human clinicians needed to spend assessing patients, while improving the quality of the assessments and freeing up other resources.

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. 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.

How are AI chatbots used in healthcare?

Chatbots provide patients with a more personalized experience, making them feel more connected to their healthcare providers. Chatbots can help patients feel more comfortable and involved in their healthcare by conversationally engaging with them. 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. As such, there are concerns about how chatbots collect, store, and use patient data. Healthcare providers must ensure that privacy laws and ethical standards handle patient data.

Furthermore, social distancing and loss of loved ones have taken a toll on people’s mental health. 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. Chatbots are software programs that use artificial intelligence and natural language processing to have personalized conversations with human users, either by text or voice. In healthcare, chatbots are being applied to automate conversations with patients for numerous uses – we‘ll cover the major ones shortly.

Taking Care of Routine Work & Automating Administrative Processes

The app users may engage in a live video or text consultation on the platform, bypassing hospital visits. 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. For example, it may be almost impossible for a healthcare chat bot to give an accurate diagnosis based on symptoms for complex conditions. While chatbots that serve as symptom checkers could accurately generate differential diagnoses of an array of symptoms, it will take a doctor, in many cases, to investigate or query further to reach an accurate diagnosis. A user interface is the meeting point between men and computers; the point where a user interacts with the design.

He and his colleagues tested ChatGPT on a number of hypothetical vignettes – the type he’s likely to ask first-year medical residents. It provided the correct diagnosis and appropriate triage recommendations about as well as doctors did and far better than the online symptom checkers  that the team tested in previous research. ChatGPT – the GPT stands for Generative Pre-trained Transformer – is an artificial intelligence platform from San Francisco-based startup OpenAI. The free online tool, trained on millions of pages of data from across the internet, generates responses to questions in a conversational tone. To identify chatbot use cases deployed for public health response activities during the Covid-19 pandemic. Ultimately, however, the further advances of artificial intelligence are fascinating, and it will be interesting to see how large language models such as ChatGPT are implemented into all aspects of life, including the healthcare industry, in the near future.

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

While many chatbots leverage risk-assessment criteria from official sources (WHO, CDC, or other government health agency), the questions asked vary significantly across chatbots, and as does the order in which they are asked. Some ask general questions about exposure and symptoms (e.g., Case 7), whereas others also check for preexisting conditions to assess high-risk users (e.g., Case 1). Based on the assessed risk, the chatbot makes behavioral recommendations (e.g., self-monitor, quarantine, etc.).

Key areas of focus are safety, effectiveness, timeliness, efficiency, equitability, and patient-centered care [20]. 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. For the output modality, or how the chatbot interacts with the user, all accessible apps had a text-based interface (98%), with five apps (6%) also allowing spoken/verbal output, and six apps (8%) supporting visual output. Visual output, in this case, included the use of an embodied avatar with modified expressions in response to user input.

However, occasionally, these technologies are presented, more or less implicitly, as replacements of the human actor on a task, suggesting that they—or their abilities/capabilities—are identifiable with human beings (or their abilities/capabilities). Survivors of cancer, particularly those who underwent treatment during childhood, are more susceptible to adverse health risks and medical complications. Consequently, promoting a healthy lifestyle early on is imperative to maintain quality of life, reduce mortality, and decrease the risk of secondary cancers [87].

The ‘rigid’ and formal systems of chatbots, even with the ML bend, are locked in certain a priori models of calculation. Expertise generally requires the intersubjective circulation of knowledge, that is, a pool of dynamic knowledge and intersubjective criticism of data, knowledge and processes (e.g. Prior 2003; Collins and Evans 2007). Therefore, AI technologies (e.g. chatbots) should not be evaluated on the same level as human beings. AI technologies can perform some narrow tasks or functions better than humans, and their calculation power is faster and memory more reliable.

Schedule Appointments and Set Reminders

Just as he wouldn’t trust a newly minted intern on their first day in the hospital to take care of him, programs like ChatGPT aren’t yet ready to deliver medical advice. But as the algorithm processes information again and again, it will continue to improve, he said. “Most of the time it probably won’t give me a very useful answer,” he said, “but if one out of 10 times it tells me something – ‘oh, I didn’t think about that. That’s a really intriguing idea!’ Then maybe it can make me a better doctor.” Mehrotra, who recently saw a patient with a confusing spectrum of symptoms, said he could envision asking ChatGPT or a similar tool for possible diagnoses.

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. The healthcare industry is constantly embracing technological advancements, as every new innovation brings significant improvements to patient care and to work processes of medical professionals. And while some innovations may be too complex or expensive to implement, there is one that is highly affordable and efficient, and it’s a healthcare chatbot. The rapid adoption of AI chatbots in healthcare leads to the rapid development of medical-oriented large language models. How do we deal with all these issues when developing a clinical chatbot for healthcare?

Step 3: Fuse the best of human and AI

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 use these LLMs are forbidden from being used in the EU. Healthcare providers can handle medical bills, insurance dealings, and claims automatically using AI-powered chatbots. Chatbots also support doctors in managing charges and the pre-authorization process. A conversational bot can examine the patient’s symptoms and offer potential diagnoses. This also helps medical professionals stay updated about any changes in patient symptoms.

• One in every twenty Google searches is about health, this clearly demonstrates the need to receive proper healthcare advice digitally.
• 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).
• However, these bots can at least help patients understand what kind of treatment to request and what might be the issue, which is already a good start.
• Although scheduling systems are in use, many patients still find it difficult to navigate the scheduling systems.
• Furthermore, we were unable to extract data regarding the number of app downloads for the Apple iOS store, only the number of ratings.

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. use of chatbots in healthcare First, we used IAB categories, classification parameters utilized by 42Matters; this relied on the correct classification of apps by 42Matters 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.

These mental health chatbots increase access to support and show promising results comparable to human-led treatment based on early studies. 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.

Health and patient safety coverage at USA TODAY is made possible in part by a grant from the Masimo Foundation for Ethics, Innovation and Competition in Healthcare. The training of GPT-3, which formed some of the basis for ChatGPT, consumed 1,287 megawatt hours of energy and led to emissions of more than 550 tons of carbon dioxide equivalent, roughly as much as three roundtrip flights between New York and San Francisco. According to EpochAI, a team of AI researchers, the cost of training an artificial intelligence model on increasingly large datasets will climb to about $500 million by 2030.

• Under this category are the open domain for general topics and the closed domain focusing on more specific information.
• And whether this technology should be available to patients, as well as doctors and researchers, and how much it should be regulated remain open questions.
• Thus, their function is to solve complex problems using reasoning methods such as the if-then-else format.
• According to the report by Zipdo, the global healthcare chatbot market is expected to reach approximately $498.5 million by 2026.

At the global health level, chatbots have emerged as a socially responsible technology to provide equal access to quality health care and break down the barriers between the rich and poor [112]. To further advance medicine and knowledge, the use of chatbots in education for learning and assessments is crucial for providing objective feedback, personalized content, and cost-effective evaluations [113]. 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. 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].