Artificial Intelligence Literacy: Developing a Multi-institutional Infrastructure for AI Education.
Rationale and Objectives: To evaluate the effectiveness of an artificial intelligence (AI) in radiology literacy course on participants from nine radiology residency programs in the Southeast and Mid-Atlantic United States.Materials and Methods: A week-long AI in radiology course was developed and i...
| Publicado en: | Academic Radiology Vol. 30; no. 7; pp. 1472 - 1481 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Jul2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164417170&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164417170 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10766332 T4X jtl: Academic Radiology issn: 10766332 maglogo: N pubinfo: dt: Jul2023 vid: 30 iid: 7 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 164417170 164417170 NLM36323613 10.1016/j.acra.2022.10.002 NLM36323613 164417170 ppf: 1472 ppct: 9 formats: tig: atl: Artificial Intelligence Literacy: Developing a Multi-institutional Infrastructure for AI Education. aug: au: Perchik, J.D. Smith, A.D. Elkassem, A.A. Park, J.M. Rothenberg, S.A. Tanwar, M. Yi, P.H. Sturdivant, A. Tridandapani, S. Sotoudeh, H. affil: Department of Diagnostic Radiology, University of Alabama at Birmingham, Birmingham, Alabama sug: ab: Rationale and Objectives: To evaluate the effectiveness of an artificial intelligence (AI) in radiology literacy course on participants from nine radiology residency programs in the Southeast and Mid-Atlantic United States.Materials and Methods: A week-long AI in radiology course was developed and included participants from nine radiology residency programs in the Southeast and Mid-Atlantic United States. Ten 30 minutes lectures utilizing a remote learning format covered basic AI terms and methods, clinical applications of AI in radiology by four different subspecialties, and special topics lectures on the economics of AI, ethics of AI, algorithm bias, and medicolegal implications of AI in medicine. A proctored hands-on clinical AI session allowed participants to directly use an FDA cleared AI-assisted viewer and reporting system for advanced cancer. Pre- and post-course electronic surveys were distributed to assess participants' knowledge of AI terminology and applications and interest in AI education.Results: There were an average of 75 participants each day of the course (range: 50-120). Nearly all participants reported a lack of sufficient exposure to AI in their radiology training (96.7%, 90/93). Mean participant score on the pre-course AI knowledge evaluation was 8.3/15, with a statistically significant increase to 10.1/15 on the post-course evaluation (p= 0.04). A majority of participants reported an interest in continued AI in radiology education in the future (78.6%, 22/28).Conclusion: A multi-institutional AI in radiology literacy course successfully improved AI education of participants, with the majority of participants reporting a continued interest in AI in radiology education in the future. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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