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

As we mentioned earlier, the collection of information is vital for the healthcare sector as it allows more personalized healthcare and, as a result, leads to more satisfied patients. Hence, these bots are really important as they help healthcare organizations evaluate their services, understand their patients better, and overall gain a better understanding of what might be improved and how. Few of the included studies discussed how they handled safeguarding issues, even if only at the design stage. This methodology is a particular concern when chatbots are used at scale or in sensitive situations such as mental health. In this respect, chatbots may be best suited as supplements to be used alongside existing medical practice rather than as replacements [21,33]. Although research on the use of chatbots in public health is at an early stage, developments in technology and the exigencies of combatting COVID-19 have contributed to the huge upswing in their use, most notably in triage roles.

Chatbots have been incorporated into health coaching systems to address health behavior modifications. For example, CoachAI and Smart Wireless Interactive Health System used chatbot technology to track patients’ progress, provide insight to physicians, and suggest suitable activities [45,46]. 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].

The Development and Use of Chatbots in Public Health: Scoping Review

Some experts also believe doctors will recommend chatbots to patients with ongoing health issues. In the future, we might share our health information with text bots to make better decisions about our health. For example, when a chatbot suggests a suitable recommendation, it makes patients feel genuinely cared for. This AI-driven technology can quickly respond to queries and sometimes even better than humans. A medical bot can recognize when a patient needs urgent help if trained and designed correctly. It can provide immediate attention from a doctor by setting appointments, especially during emergencies.

Inter-agent chatbots become omnipresent while all chatbots will require some inter-chatbot communication possibilities. Natural Language Processing (NLP), an area of artificial intelligence, explores the manipulation of natural language text or speech by computers. Knowledge of the understanding and use of human language is gathered to develop techniques that will make computers understand and manipulate natural expressions to perform desired tasks [32]. The reduction in customer service costs and the ability to handle many users at a time are some of the reasons why chatbots have become so popular in business groups [20]. Chatbots are no longer seen as mere assistants, and their way of interacting brings them closer to users as friendly companions [21]. According to a study, social media user requests on chatbots for customer service are emotional and informational, with the first category rate being more than 40% and with users not intending to take specific information [22].

Types of Chatbots

Response generation chatbots, further classified as rule based, retrieval based, and generative, account for the process of analyzing inputs and generating responses [16]. Finally, human-aided classification incorporates human chatbot technology in healthcare computation, which provides more flexibility and robustness but lacks the speed to accommodate more requests [17]. Woebot is among the best examples of chatbots in healthcare in the context of a mental health support solution.