{"title":"Social networks in radiology: Toward a new paradigm in medical education?","authors":"J.L. del Cura Rodríguez","doi":"10.1016/j.rxeng.2023.01.011","DOIUrl":null,"url":null,"abstract":"<div><p>The universally accepted system for the transmission of scientific knowledge in the field of medicine has long been grounded in scientific publications. Social networks can be a useful alternative or complementary method of transmitting this knowledge.</p><p>Social networks (e.g., Twitter, Instagram, Facebook, LinkedIn, YouTube, and TikTok) generate educational contents that enable quality training, despite their informality. Each of these networks has strengths and weaknesses that users should know about.</p><p>These platforms are free and allow for real-time discussion. They make it easy to incorporate content and to contact experts or access sources of knowledge directly. Aware of their influence, publishers have incorporated metrics to measure the impact of their articles in social networks (Altmetrics).</p><p>These networks should be incorporated into departmental training programs immediately. Nevertheless, navigating through social networks is complex, and the hashtag-based system of searching is inefficient, limiting their use in education.</p><p>Despite the informality of the knowledge generated on social networks, the importance of these networks as a source of knowledge is growing. Radiology departments must design a strategy for using social networks for education rather than for propaganda, creating well-organized focal groups that search for contents through systematic, filtered review of information, digital repositories, and review sessions and for sharing this knowledge both inside and outside the department. Departments must also implement a strategy for communicating through these networks.</p></div>","PeriodicalId":94185,"journal":{"name":"Radiologia","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiologia","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2173510724000181","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The universally accepted system for the transmission of scientific knowledge in the field of medicine has long been grounded in scientific publications. Social networks can be a useful alternative or complementary method of transmitting this knowledge.
Social networks (e.g., Twitter, Instagram, Facebook, LinkedIn, YouTube, and TikTok) generate educational contents that enable quality training, despite their informality. Each of these networks has strengths and weaknesses that users should know about.
These platforms are free and allow for real-time discussion. They make it easy to incorporate content and to contact experts or access sources of knowledge directly. Aware of their influence, publishers have incorporated metrics to measure the impact of their articles in social networks (Altmetrics).
These networks should be incorporated into departmental training programs immediately. Nevertheless, navigating through social networks is complex, and the hashtag-based system of searching is inefficient, limiting their use in education.
Despite the informality of the knowledge generated on social networks, the importance of these networks as a source of knowledge is growing. Radiology departments must design a strategy for using social networks for education rather than for propaganda, creating well-organized focal groups that search for contents through systematic, filtered review of information, digital repositories, and review sessions and for sharing this knowledge both inside and outside the department. Departments must also implement a strategy for communicating through these networks.