FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4297812028> ?p ?o ?g. }

• W4297812028 endingPage "e38325" @default.
• W4297812028 startingPage "e38325" @default.
• W4297812028 abstract "Background Given the rapidity with which artificial intelligence is gaining momentum in clinical medicine, current physician leaders have called for more incorporation of artificial intelligence topics into undergraduate medical education. This is to prepare future physicians to better work together with artificial intelligence technology. However, the first step in curriculum development is to survey the needs of end users. There has not been a study to determine which media and which topics are most preferred by US medical students to learn about the topic of artificial intelligence in medicine. Objective We aimed to survey US medical students on the need to incorporate artificial intelligence in undergraduate medical education and their preferred means to do so to assist with future education initiatives. Methods A mixed methods survey comprising both specific questions and a write-in response section was sent through Qualtrics to US medical students in May 2021. Likert scale questions were used to first assess various perceptions of artificial intelligence in medicine. Specific questions were posed regarding learning format and topics in artificial intelligence. Results We surveyed 390 US medical students with an average age of 26 (SD 3) years from 17 different medical programs (the estimated response rate was 3.5%). A majority (355/388, 91.5%) of respondents agreed that training in artificial intelligence concepts during medical school would be useful for their future. While 79.4% (308/388) were excited to use artificial intelligence technologies, 91.2% (353/387) either reported that their medical schools did not offer resources or were unsure if they did so. Short lectures (264/378, 69.8%), formal electives (180/378, 47.6%), and Q and A panels (167/378, 44.2%) were identified as preferred formats, while fundamental concepts of artificial intelligence (247/379, 65.2%), when to use artificial intelligence in medicine (227/379, 59.9%), and pros and cons of using artificial intelligence (224/379, 59.1%) were the most preferred topics for enhancing their training. Conclusions The results of this study indicate that current US medical students recognize the importance of artificial intelligence in medicine and acknowledge that current formal education and resources to study artificial intelligence–related topics are limited in most US medical schools. Respondents also indicated that a hybrid formal/flexible format would be most appropriate for incorporating artificial intelligence as a topic in US medical schools. Based on these data, we conclude that there is a definitive knowledge gap in artificial intelligence education within current medical education in the US. Further, the results suggest there is a disparity in opinions on the specific format and topics to be introduced." @default.
• W4297812028 created "2022-10-01" @default.
• W4297812028 creator A5000681298 @default.
• W4297812028 creator A5010594647 @default.
• W4297812028 creator A5010868531 @default.
• W4297812028 creator A5012650285 @default.
• W4297812028 creator A5014451266 @default.
• W4297812028 creator A5025916649 @default.
• W4297812028 creator A5028604346 @default.
• W4297812028 creator A5065966007 @default.
• W4297812028 date "2022-10-21" @default.
• W4297812028 modified "2023-10-16" @default.
• W4297812028 title "Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study" @default.
• W4297812028 cites W2119481955 @default.
• W4297812028 cites W2558050786 @default.
• W4297812028 cites W2576404523 @default.
• W4297812028 cites W2751912040 @default.
• W4297812028 cites W2788948370 @default.
• W4297812028 cites W2791063712 @default.
• W4297812028 cites W2792248972 @default.
• W4297812028 cites W2855469196 @default.
• W4297812028 cites W2889976627 @default.
• W4297812028 cites W2900248733 @default.
• W4297812028 cites W2901547687 @default.
• W4297812028 cites W2901954625 @default.
• W4297812028 cites W2903800844 @default.
• W4297812028 cites W2908201961 @default.
• W4297812028 cites W2913847760 @default.
• W4297812028 cites W2914069220 @default.
• W4297812028 cites W2916492225 @default.
• W4297812028 cites W2953532875 @default.
• W4297812028 cites W2954683258 @default.
• W4297812028 cites W2981296841 @default.
• W4297812028 cites W2994545764 @default.
• W4297812028 cites W3006767019 @default.
• W4297812028 cites W3006814580 @default.
• W4297812028 cites W3012668909 @default.
• W4297812028 cites W3013758358 @default.
• W4297812028 cites W3035816197 @default.
• W4297812028 cites W3037873741 @default.
• W4297812028 cites W3041967878 @default.
• W4297812028 cites W3047336156 @default.
• W4297812028 cites W3083782901 @default.
• W4297812028 cites W3093404209 @default.
• W4297812028 cites W3094858067 @default.
• W4297812028 cites W3095523094 @default.
• W4297812028 cites W3108873103 @default.
• W4297812028 cites W3119041287 @default.
• W4297812028 cites W3161617170 @default.
• W4297812028 cites W3173900512 @default.
• W4297812028 cites W3197755468 @default.
• W4297812028 cites W3210833402 @default.
• W4297812028 cites W3134864973 @default.
• W4297812028 doi "https://doi.org/10.2196/38325" @default.
• W4297812028 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36269641" @default.
• W4297812028 hasPublicationYear "2022" @default.
• W4297812028 type Work @default.
• W4297812028 citedByCount "7" @default.
• W4297812028 countsByYear W42978120282023 @default.
• W4297812028 crossrefType "journal-article" @default.
• W4297812028 hasAuthorship W4297812028A5000681298 @default.
• W4297812028 hasAuthorship W4297812028A5010594647 @default.
• W4297812028 hasAuthorship W4297812028A5010868531 @default.
• W4297812028 hasAuthorship W4297812028A5012650285 @default.
• W4297812028 hasAuthorship W4297812028A5014451266 @default.
• W4297812028 hasAuthorship W4297812028A5025916649 @default.
• W4297812028 hasAuthorship W4297812028A5028604346 @default.
• W4297812028 hasAuthorship W4297812028A5065966007 @default.
• W4297812028 hasBestOaLocation W42978120281 @default.
• W4297812028 hasConcept C105776082 @default.
• W4297812028 hasConcept C138496976 @default.
• W4297812028 hasConcept C154945302 @default.
• W4297812028 hasConcept C15744967 @default.
• W4297812028 hasConcept C169760540 @default.
• W4297812028 hasConcept C19417346 @default.
• W4297812028 hasConcept C26760741 @default.
• W4297812028 hasConcept C41008148 @default.
• W4297812028 hasConcept C47177190 @default.
• W4297812028 hasConcept C509550671 @default.
• W4297812028 hasConcept C71924100 @default.
• W4297812028 hasConceptScore W4297812028C105776082 @default.
• W4297812028 hasConceptScore W4297812028C138496976 @default.
• W4297812028 hasConceptScore W4297812028C154945302 @default.
• W4297812028 hasConceptScore W4297812028C15744967 @default.
• W4297812028 hasConceptScore W4297812028C169760540 @default.
• W4297812028 hasConceptScore W4297812028C19417346 @default.
• W4297812028 hasConceptScore W4297812028C26760741 @default.
• W4297812028 hasConceptScore W4297812028C41008148 @default.
• W4297812028 hasConceptScore W4297812028C47177190 @default.
• W4297812028 hasConceptScore W4297812028C509550671 @default.
• W4297812028 hasConceptScore W4297812028C71924100 @default.
• W4297812028 hasIssue "4" @default.
• W4297812028 hasLocation W42978120281 @default.
• W4297812028 hasLocation W42978120282 @default.
• W4297812028 hasLocation W42978120283 @default.
• W4297812028 hasOpenAccess W4297812028 @default.
• W4297812028 hasPrimaryLocation W42978120281 @default.
• W4297812028 hasRelatedWork W1996318672 @default.

• next