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

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. Eighty-two percent of apps had a specific task for the user to focus on (i.e., entering symptoms). 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.

Not only do these responses defeat the purpose of the conversation, chatbots in healthcare industry but they also make the conversation one-sided and unnatural.

Monitoring patients

Two of the most popular chatbots used in health care are the mental health assistant Woebot and Omaolo, which is used in Finland. From the emergence of the first chatbot, ELIZA, developed by Joseph Weizenbaum (1966), chatbots have been trying to ‘mimic human behaviour in a text-based conversation’ (Shum et al. 2018, p. 10; Abd-Alrazaq et al. 2020). Thus, their key feature is language and speech recognition, that is, natural language processing (NLP), which enables them to understand, to a certain extent, the language of the user (Gentner et al. 2020, p. 2). Despite the initial chatbot hype dwindling down, medical chatbots still have the potential to improve the healthcare industry. The three main areas where they can be particularly useful include diagnostics, patient engagement outside medical facilities, and mental health. At least, that’s what CB Insights analysts are bringing forward in their healthcare chatbot market research, generally saying that the future of chatbots in the healthcare industry looks bright.

By harnessing the power of Generative Conversational AI, medical institutions are rewriting the rules of patient engagement. We are witnessing a rapid upsurge in the development and implementation of various AI solutions in the healthcare sector. It’s also not realistic to expect every patient to be on board with digital-care solutions beyond their current use in this pandemic. Such self-diagnosis may become such a routine affair as to hinder the patient from accessing medical care when it is truly necessary, or believing medical professionals when it becomes clear that the self-diagnosis was inaccurate.

Use cases of medical chatbots

A revolutionary move that makes way for new treatment methods and discoveries with the help of AI-powered chatbots. They do not necessarily connect with the patients but also with the care providers in the case of children and the elderly. Aged people should often visit hospitals; even in this scenario, chatbots assist if it is a primary treatment or consultation.

• Without training data, your bot would simply respond using the same string of text over and over again without understanding what it is doing.
• The chatbots can help them by listening to their concerns and providing necessary answers or solutions.
• O’Meara shares insights into Ochre Bio’s innovative RNA therapies, their approach to tackling liver disease, and the company’s vision for the future.
• Added life expectancy poses new challenges for both patients and the health care team.
• QliqSOFT’s Quincy chatbot solution, which is powered by an AI engine and driven by natural-language processing, enables real-time, patient-centered collaboration through text messaging.