Improvements were made to build a more human-like and personalized entity by incorporating a personality in PARRY (developed Kenneth Colby) that simulated a paranoid patient. In 1966, ELIZA (MIT Artificial Intelligence Library) was the first known chatbot developed to act as a psychotherapist, using pattern matching and template-based responses to converse in a question-based format. The earliest forms were designed to pass the Turing test and mimic human conversations as much as possible. The idea of a chatbot was first introduced in 1950 when Alan Turing proposed the question, “Can machines think?”.
Thus, the results from equivalent studies may differ when repeated. It should be noted that using the health filters from a web directory limits the results to the search strategy and marketing label. A total of 78 chatbots were identified for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. This directory was chosen as it was open-access and categorized the chatbots under many different categories (ie, health care, communication, and entertainment) and contained many commonly used messaging services (ie, Facebook Messenger, Discord, Slack, Kik, and Skype). The screening of chatbots was guided by a systematic review process from the Botlist directory during the period of January 2021.
AI CHATBOT FOR HEALTHCARE FULL
The full list of sources and search strategies is available from the authors. Letters and technical reports were excluded from the search. The searches were not limited by language or study design. This included healthcare, cancer therapy, oncology, diagnosis, treatment, radiation therapy, and radiotherapy. For further refinement, these key terms were combined with more specific terms aligned with the focus of the paper. The literature search used the following key terms: chatbot, chatter robot, conversational agent, artificial intelligence, and machine learning. The following databases were searched from October to December 2020 for relevant and current studies from 2000 to 2020: IEEE Xplore, PubMed, Web of Science, Scopus, and OVID. This review focuses on articles from peer-reviewed journals and conference proceedings. Concerns regarding accuracy, cybersecurity, lack of empathy, and technological maturity are reported as potential factors associated with the delay in chatbot acceptability or integration into health care. In light of the opportunities provided by this relatively new technology, potential limitations and areas of concern may arise that could potentially harm users. A web-based, self-report survey examining physicians’ perspectives found positive benefits of health care chatbots in managing one’s own health for improved physical, psychological, and behavioral outcomes and most notably, for administrative purposes. With their ability for complex dialog management and conversational flexibility, integration of chatbot technology into clinical practice may reduce costs, refine workflow efficiencies, and improve patient outcomes.
Chatbots have been proven to be particularly applicable in various health care components that usually involve face-to-face interactions. Given these effectual benefits, it is not surprising that chatbots have rapidly evolved over the past 2 decades and integrated themselves into numerous fields, such as entertainment, travel, gaming, robotics, and security. Problems arise when dealing with more complex situations in dynamic environments and managing social conversational practices according to specific contexts and unique communication strategies. Predetermined responses are then generated by analyzing user input, on text or spoken ground, and accessing relevant knowledge. ” They can also be physical entities designed to socially interact with humans or other robots. The Oxford dictionary defines a chatbot as “a computer program that can hold a conversation with a person, usually over the internet. Chatbots, also known as chatter robots, smart bots, conversational agents, digital assistants, or intellectual agents, are prime examples of AI systems that have evolved from ML. Advancements in ML have provided benefits in terms of accuracy, decision-making, quick processing, cost-effectiveness, and handling of complex data. Machine learning (ML) is a subset of AI that improves its performance based on the data provided to a generic algorithm from experience rather than defining rules in traditional approaches. Artificial intelligence (AI) is at the forefront of transforming numerous aspects of our lives by modifying the way we analyze information and improving decision-making through problem solving, reasoning, and learning.
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