The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review

Milne‐Ives, Madison; Cock, Caroline de; Lim, Ernest; Shehadeh, Melissa Harper; Pennington, Nick de; Mole, Guy; Normando, Eduardo; Meinert, Edward

doi:10.2196/20346

Cited by 270 publications

(260 citation statements)

References 54 publications

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“…The demand for health care services is at a maximum level this year since the outbreak of COVID-19. Many researchers and developers are now looking for ways to integrate the chatbot as conversational agents to support health-related activities, including increasing health literacy (Milne-Ives et al, 2020). IHAB's performance was evaluated by testing the application for 20 days and exploring and communicating regarding health issues.…”

Section: Discussionmentioning

confidence: 99%

The evaluation of chatbot as a tool for health literacy education among undergraduate students

Mokmin

Ibrahim

2021

Educ Inf Technol

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This study discussed and evaluated the usefulness, performance, and technology acceptance of a chatbot developed to educate users and provide health literacy. A semi-structured interview and analytic sessions were provided on Google Analytics dashboard, and the users' acceptance toward the technology was measured using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). A total of 75 undergraduate students were involved over a total period of two months. Each respondent explored the health chatbot actively to get advice from it with a phrase that matched the chatbot's intents via mobile devices. The evaluation results showed that 73.3% of the respondents found that the chatbot can help understand several health issues and provide a good conversation. The performance evaluation also showed that the chatbot contributed a low percentage of exit, where less than 37% of users exited the application. The overall assessment showed that the developed chatbot has a significant potential to be used as a conversational agent to increase health literacy, especially among students and young adults. However, more research should be done before the technology can replace humans in a real setting.

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Section: Discussionmentioning

confidence: 99%

The evaluation of chatbot as a tool for health literacy education among undergraduate students

Mokmin

Ibrahim

2021

Educ Inf Technol

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“…The categories identified in this study could be used as a reference for patient education in future. Currently, only one chatbot is available to empower patients with cancer and their [6,15,16]. Given that patients with breast cancer and those with lung cancer differ in their age of onset, chemotherapy regimens, and cancer-related symptoms [17], it is important to increase the number of categories to improve their knowledge of symptom management based on the FAQs identified in this study.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

“…Chatbots are artificial intelligence (AI)-driven programs that interact with people [5] through text messages and outputs on cellphone-or web-based platforms [6]. A simple rendition of this system involves a user entering text and AI predicting the corresponding predefined category and then sending the response corresponding to that category to the user.…”

Section: Introductionmentioning

confidence: 99%

Development and Early Feasibility of Chatbots for Educating Patients With Lung Cancer and Their Caregivers in Japan: Mixed Methods Study

Kataoka¹,

Takemura²,

Sasajima³

et al. 2021

JMIR Cancer

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Background Chatbots are artificial intelligence–driven programs that interact with people. The applications of this technology include the collection and delivery of information, generation of and responding to inquiries, collection of end user feedback, and the delivery of personalized health and medical information to patients through cellphone- and web-based platforms. However, no chatbots have been developed for patients with lung cancer and their caregivers. Objective This study aimed to develop and evaluate the early feasibility of a chatbot designed to improve the knowledge of symptom management among patients with lung cancer in Japan and their caregivers. Methods We conducted a sequential mixed methods study that included a web-based anonymized questionnaire survey administered to physicians and paramedics from June to July 2019 (phase 1). Two physicians conducted a content analysis of the questionnaire to curate frequently asked questions (FAQs; phase 2). Based on these FAQs, we developed and integrated a chatbot into a social network service (phase 3). The physicians and paramedics involved in phase I then tested this chatbot (α test; phase 4). Thereafter, patients with lung cancer and their caregivers tested this chatbot (β test; phase 5). Results We obtained 246 questions from 15 health care providers in phase 1. We curated 91 FAQs and their corresponding responses in phase 2. In total, 11 patients and 1 caregiver participated in the β test in phase 5. The participants were asked 60 questions, 8 (13%) of which did not match the appropriate categories. After the β test, 7 (64%) participants responded to the postexperimental questionnaire. The mean satisfaction score was 2.7 (SD 0.5) points out of 5. Conclusions Medical staff providing care to patients with lung cancer can use the categories specified in this chatbot to educate patients on how they can manage their symptoms. Further studies are required to improve chatbots in terms of interaction with patients.

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“…The advantages of an AI chatbot over human HCPs include the absence of face-to-face interaction; minimization of bias based on certain patient demographic characteristics such as age, gender, and race; greater cost-effectiveness; and 24/7 availability since the chatbot does not get fatigued or sick [ 5 ]. In a recent systematic review on the effectiveness of AI chatbots in health care, the bots performed well in terms of both usability and satisfaction, and overall positive or mixed effectiveness was reported in most studies [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment

Lee¹,

Kang²,

Yeo³

2021

J Med Internet Res

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Background The COVID-19 pandemic has limited daily activities and even contact between patients and primary care providers. This makes it more difficult to provide adequate primary care services, which include connecting patients to an appropriate medical specialist. A smartphone-compatible artificial intelligence (AI) chatbot that classifies patients’ symptoms and recommends the appropriate medical specialty could provide a valuable solution. Objective In order to establish a contactless method of recommending the appropriate medical specialty, this study aimed to construct a deep learning–based natural language processing (NLP) pipeline and to develop an AI chatbot that can be used on a smartphone. Methods We collected 118,008 sentences containing information on symptoms with labels (medical specialty), conducted data cleansing, and finally constructed a pipeline of 51,134 sentences for this study. Several deep learning models, including 4 different long short-term memory (LSTM) models with or without attention and with or without a pretrained FastText embedding layer, as well as bidirectional encoder representations from transformers for NLP, were trained and validated using a randomly selected test data set. The performance of the models was evaluated on the basis of the precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). An AI chatbot was also designed to make it easy for patients to use this specialty recommendation system. We used an open-source framework called “Alpha” to develop our AI chatbot. This takes the form of a web-based app with a frontend chat interface capable of conversing in text and a backend cloud-based server application to handle data collection, process the data with a deep learning model, and offer the medical specialty recommendation in a responsive web that is compatible with both desktops and smartphones. Results The bidirectional encoder representations from transformers model yielded the best performance, with an AUC of 0.964 and F1-score of 0.768, followed by LSTM model with embedding vectors, with an AUC of 0.965 and F1-score of 0.739. Considering the limitations of computing resources and the wide availability of smartphones, the LSTM model with embedding vectors trained on our data set was adopted for our AI chatbot service. We also deployed an Alpha version of the AI chatbot to be executed on both desktops and smartphones. Conclusions With the increasing need for telemedicine during the current COVID-19 pandemic, an AI chatbot with a deep learning–based NLP model that can recommend a medical specialty to patients through their smartphones would be exceedingly useful. This chatbot allows patients to identify the proper medical specialist in a rapid and contactless manner, based on their symptoms, thus potentially supporting both patients and primary care providers.

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The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review

Cited by 270 publications

References 54 publications

The evaluation of chatbot as a tool for health literacy education among undergraduate students

The evaluation of chatbot as a tool for health literacy education among undergraduate students

Development and Early Feasibility of Chatbots for Educating Patients With Lung Cancer and Their Caregivers in Japan: Mixed Methods Study

Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment

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