Transforming Health Care Through Chatbots for Medical History-Taking and Future Directions: Comprehensive Systematic Review

Abstract

Background: The integration of artificial intelligence and chatbot technology in health care has attracted significant attention due to its potential to improve patient care and streamline history-taking. As artificial intelligence-driven conversational agents, chatbots offer the opportunity to revolutionize history-taking, necessitating a comprehensive examination of their impact on medical practice.

Objective: This systematic review aims to assess the role, effectiveness, usability, and patient acceptance of chatbots in medical history-taking. It also examines potential challenges and future opportunities for integration into clinical practice.

Methods: A systematic search included PubMed, Embase, MEDLINE (via Ovid), CENTRAL, Scopus, and Open Science and covered studies through July 2024. The inclusion and exclusion criteria for the studies reviewed were based on the PICOS (participants, interventions, comparators, outcomes, and study design) framework. The population included individuals using health care chatbots for medical history-taking. Interventions focused on chatbots designed to facilitate medical history-taking. The outcomes of interest were the feasibility, acceptance, and usability of chatbot-based medical history-taking. Studies not reporting on these outcomes were excluded. All study designs except conference papers were eligible for inclusion. Only English-language studies were considered. There were no specific restrictions on study duration. Key search terms included "chatbot*," "conversational agent*," "virtual assistant," "artificial intelligence chatbot," "medical history," and "history-taking." The quality of observational studies was classified using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria (eg, sample size, design, data collection, and follow-up). The RoB 2 (Risk of Bias) tool assessed areas and the levels of bias in randomized controlled trials (RCTs).

Results: The review included 15 observational studies and 3 RCTs and synthesized evidence from different medical fields and populations. Chatbots systematically collect information through targeted queries and data retrieval, improving patient engagement and satisfaction. The results show that chatbots have great potential for history-taking and that the efficiency and accessibility of the health care system can be improved by 24/7 automated data collection. Bias assessments revealed that of the 15 observational studies, 5 (33%) studies were of high quality, 5 (33%) studies were of moderate quality, and 5 (33%) studies were of low quality. Of the RCTs, 2 had a low risk of bias, while 1 had a high risk.

Conclusions: This systematic review provides critical insights into the potential benefits and challenges of using chatbots for medical history-taking. The included studies showed that chatbots can increase patient engagement, streamline data collection, and improve health care decision-making. For effective integration into clinical practice, it is crucial to design user-friendly interfaces, ensure robust data security, and maintain empathetic patient-physician interactions. Future research should focus on refining chatbot algorithms, improving their emotional intelligence, and extending their application to different health care settings to realize their full potential in modern medicine.

Trial registration: PROSPERO CRD42023410312; www.crd.york.ac.uk/prospero.

Keywords: acceptability; artificial intelligence; chatbots; clinical decision-making; conversational agents; cybersecurity; diagnostic accuracy; health care data collection; health informatics; machine learning; medical history-taking; natural language processing; patient engagement; patient-doctor communication; systematic review; usability.

Figure 1

Number of studies over recent years: “chatbot*” AND “medicine.” This chart shows the increasing trend in publications on chatbots in medicine from 2017 to 2023. In 2022, there was an exponential increase in published studies, indicating a growing research interest and progress in chatbots in medicine.

Figure 2

Flowchart of the study search and inclusion. This flowchart details the systematic process of selecting studies for the review, starting from 203 records and narrowing down to 18 studies after removing duplicates and applying eligibility criteria. IEEE: Institute of Electrical and Electronic Engineers.

Figure 3

Alluvial diagram of the publication date, country, and area of studies. The alluvial diagram illustrates the distribution of studies by year, country, and medical area from 2015 to 2023, highlighting increased publications in 2020 and 2022, with contributions from Germany, the United States, and Switzerland across various medical fields.

Figure 4

World map showing the number of studies published in each country. This map shows the geographical distribution of the studies, with most research originating from Germany and the United States. Created with MapChart [55].

Figure 5

Fulfillment of STROBE criteria and categorization. This bar chart categorizes observational studies by their adherence to STROBE criteria, showing 37.5% of high-quality (category A), and an even split between moderate (category B) and lower quality (category C). STROBE: Strengthening the Reporting of Observational Studies in Epidemiology.

Figure 6

Risk of bias domains (RoB-tool) for randomized controlled trials.