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

Depending on the specific use case scenario, chatbots possess various levels of intelligence and have datasets of different sizes at their disposal. The 61 chatbots reflect a global sample of chatbots deployed in more than 30 countries. These include 33 chatbots that chatbot use cases in healthcare conversed in 45 languages other than (or in addition to) English. Tables 1 and 2 in Appendix 1 provide background information on each chatbot, its use cases, and design features. References to case numbers below refer to the corresponding chatbots in Appendix 1.

Use cases should be defined in advance, involving business analysts and software engineers. Ada is an app-based symptom checker created by medical professionals, featuring a comprehensive medical library on the app. The search initially yielded 2293 apps from both the Apple iOS and Google Play stores (see Fig. 1). Questions like these are very important, but they may be answered without a specialist. A chatbot is able to walk the patient through post-op procedures, inform him about what to expect, and apprise him when to make contact for medical help.

CURRENT HEALTHCARE CHALLENGES AND PROPOSED SOLUTIONS

Coupled with the popularity of social media, this fueled deployment on social media apps such as Line, WhatsApp, Facebook Messenger, WeChat, and Telegram. Furthermore, accessibility via both smartphones and personal computers makes such chatbots widely available. Though a minority, we highlight the importance of SMS-based and phone-call-based chatbots to bridge the digital divide and reach people who lack access to smartphones or reliable internet connections or lack the skills to use technology.

A medical bot is created with the help of machine learning and large language models (LLMs). But, front-facing innovations like chatbot technology have the power to solve long-existing issues in the healthcare sector by increasing access to medical services and facilitating useful dialogue between patient and provider. A healthcare chatbot can also be used to quickly triage users who require urgent care by helping patients identify the severity of their symptoms and providing advice on when to seek professional help. Chatbots can recognize warning signs of mental health issues, such as depression and anxiety, through conversational analysis. This enables medical services to intervene earlier on in cases where a patient may be at risk of developing a mental health condition or require further support.

Looking for experienced software engineers?

You can improve your spending habits with the first two and increase your account’s security with the last one. People can add transactions to the created expense report directly from the bot to make the tracking even more accurate. Depending on the relevance of the report, users can also either approve or reject it. Another great chatbot use case in banking is that they can track users’ expenses and create reports from them. Another example of a chatbot use case on social media is Lyft which enabled its clients to order a ride straight from Facebook Messenger or Slack.

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. Thirty chatbots were embedded within a specific organization’s platform (e.g., Case 1, Clara on the CDC’s website).

Not only can customers book through the chatbot, but they can also ask questions about the tests that will be conducted and get answers in real time. Since a chatbot is available at all hours, users are able to access medical services or information when it’s most convenient for them, reducing the burden on staff. Here are five ways the healthcare industry is already using chatbots to maximize their efficiency and boost standards of patient care.

This provides you with relevant data and ensures your customers are happy with their experience on your site. You can use ecommerce chatbots to ease the ordering and refunding processes for your customers. Also, if you connect your ecommerce to the bots, they can check the inventory status and product availability of specific items, help customers complete purchases, and track orders. Both of these use cases of chatbots can help you increase sales and conversion rates.

HR Chatbot Use Cases

After they schedule an appointment, the bot can send a calendar invitation for the patient to remember about the visit. This is one of the chatbot healthcare use cases that serves the patient and makes the processes easier for them. It’s also very quick and simple to set up the bot, so any one of your patients can do this in under five minutes. The chatbot instructs the user how to add their medication and give details about dosing times and amounts. Straight after all that is set, the patient will start getting friendly reminders about their medication at the set times, so their health can start improving progressively. It used a chatbot to address misunderstandings and concerns about the colonoscopy and encourage more patients to follow through with the procedure.

• In conclusion, while the chatbot technology is not new, we discussed how its potential can be extended with IoTs and knowledge graphs.
• Our data collection was supplemented by accessing these chatbots to gather more information about their design and use.
• Nonetheless, we consider it important to raise this point when talking about chatbots and their potential breakthrough in health care.
• The chatbot can gather real-time data from frontline workers to enable provision of essential support, answer their questions, and provide them with real-time information.

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). 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.

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. The application should be in line with up-to-date medical regulations, ethical codes and research data.

In fact, nearly 46% of consumers expect bots to deliver an immediate response to their questions. Also, getting a quick answer is also the number one use case for chatbots according to customers. They can take over common inquiries, such as questions about shipping and pricing.

This is a challenging task as humans have developed languages over thousands of years to communicate information and ideas. NLP algorithms work to convert human language into a form that machines can comprehend, involving processes like converting text into binary vectors and creating a matrix representation of sentences. Through this, the system can extract the intended meaning and generate appropriate responses. By adding a healthcare chatbot to your customer support, you can combat the challenges effectively and give the scalability to handle conversations in real-time. Leveraging chatbot for healthcare help to know what your patients think about your hospital, doctors, treatment, and overall experience through a simple, automated conversation flow.

Twenty of these apps (25.6%) had faulty elements such as providing irrelevant responses, frozen chats, and messages, or broken/unintelligible English. Three of the apps were not fully assessed because their healthbots were non-functional. The study focused on health-related apps that had an embedded text-based conversational agent and were available for free public download through the Google Play or Apple iOS store, and available in English. A healthbot was defined as a health-related conversational agent that facilitated a bidirectional (two-way) conversation. Applications that only sent in-app text reminders and did not receive any text input from the user were excluded. Apps were also excluded if they were specific to an event (i.e., apps for conferences or marches).

Another, at the University of California San Francisco (Case 12), sends an email or text to employees to remind them to use the chatbot to get an entry pass (e.g., prior to their shift) that clears them to report to work. Education and information about nonpharmaceutical interventions was the most common combination with 15 chatbots providing both, but as Table 2 shows, chatbots exhibited a wide range of configurations of information dissemination use cases. Apart from our sponsor Zoho SalesIQ, chatbots are sorted by category and functionality. These categories can be divided into general health advice and chatbots working in specific areas (mental, cancer). If the condition is not too severe, a chatbot can help by asking a few simple questions and comparing the answers with the patient’s medical history.