Scabies is a World Health Organization‐defined neglected tropical disease, with continuously rising incidence worldwide in recent years. The aim of this study was to provide an update of the worldwide prevalence and new treatment approaches of scabies in population‐based settings. MEDLINE (PubMed), Embase and LILACS databases were reviewed for English and German language population‐based studies from October 2014 to March 2022. Two authors independently screened the records for eligibility, extracted all data and one critically appraised the quality of the studies and risk of bias. Systematic review registration: PROSPERO CRD42021247140. Overall, 1273 records were identified through database searching, of which 43 studies were included for the systematic review. Most of the studies (n = 31) examined the scabies prevalence in medium or low human development index countries. The highest prevalence of scabies reported in the general population (children and adults) was recorded in five randomly selected communities in Ghana (71.0%), whereas the highest scabies prevalence in studies, which only examined children (76.9%), was recorded in an Indonesian boarding school. The lowest prevalence was recorded in Uganda (0.18%). The systematic review highlights the prevalence of scabies worldwide, showing that scabies is still a serious, increasing disease that occurs globally and is clustered in developing countries. More transparent data on scabies prevalence are needed to identify risk factors to find new prevention measures.

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