Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey

249 indexed citations
published 2019

Countries where authors are citing Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey

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Citations

This map shows the geographic impact of Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey more than expected).

Fields of papers citing Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey.

About Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey

This paper, published in 2019, received 249 indexed citations . Written by Adam Palanica, Anirudh Thommandram, Michael H. Li and Yan Fossat covering the research area of General Health Professions and Applied Psychology. It is primarily cited by scholars working on Artificial Intelligence (108 citations), Applied Psychology (94 citations) and General Health Professions (79 citations). Published in Journal of Medical Internet Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.2196/12887.

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