AI Chatbots in Healthcare Examples + Development Guide

Their results suggest that the primary factor driving patient response to COVID-19 screening hotlines (human or chatbot) were users’ perceptions of the agent’s ability (Dennis et al. 2020, p. 1730). A secondary factor in persuasiveness, satisfaction, likelihood of following the agent’s advice and likelihood of use was the type of agent, with participants reporting that they viewed chatbots more positively in comparison with human agents. One of the positive aspects is that healthcare organisations struggling to meet user demand for screening services can provide new patient services. However, one of the downsides is patients’ overconfidence in the ability of chatbots, which can undermine confidence in physician evaluations.

According to the analysis from the web directory, health promotion chatbots are the most commonly available; however, most of them are only available on a single platform. Thus, interoperability on multiple common platforms is essential for adoption by various types of users across different age groups. In addition, voice and image recognition should also be considered, as most chatbot use cases in healthcare chatbots are still text based. Further refinements and large-scale implementations are still required to determine the benefits across different populations and sectors in health care [26]. Although overall satisfaction is found to be relatively high, there is still room for improvement by taking into account user feedback tailored to the patient’s changing needs during recovery.

AI Chatbots in Banking Sector : Benefits & Future

Therefore, the biggest challenge that healthcare chatbot developers face is ensuring the accuracy of responses. With all the benefits of AI-powered chatbots in healthcare, there are bound to be some downfalls. The biggest disadvantage of chatbots in healthcare are the potential biases in their responses. Although there is no human error here, there can still be discrepancies that lead to misdiagnoses.

For example, the workflow can be streamlined by assisting physicians in administrative tasks, such as scheduling appointments, providing medical information, or locating clinics. Contemporary implementations of chatbot technologies do not understand conversation narrative and demonstrate very limited cognitive capabilities and commonsense reasoning. Chatbot technology can acquire a context-aware (i.e., patient’s context), domain-specific (i.e., health domain) knowledge graph (extracted and integrated from external sources such as Web of Data) termed HKG. The HKG can be updated and synchronized by the evolution of Web of Data or relevant knowledge sources. HKG provides essential facts (background knowledge) that are necessary for response generation, reasoning, and inference components of chatbot engine.

EXPERT-RECOMMENDED AI CHATBOT IDEAS

Both chatbots have algorithms that calculate input data and become increasingly smarter when people use the respective platforms. The increasing use of bots in health care—and AI in general—can be attributed to, for example, advances in machine learning (ML) and increases in text-based interaction (e.g. messaging, social media, etc.) (Nordheim et al. 2019, p. 5). Chatbots are based on combining algorithms and data through the use of ML techniques. Their function is thought to be the delivery of new information or a new perspective. However, in general, AI applications such as chatbots function as tools for ensuring that available information in the evidence base is properly considered.