{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T17:38:21Z","timestamp":1792604301904,"version":"4.5.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Radiology and breast imaging curricula have relied almost entirely on passive learning tools such as lectures, books, journal articles, and videos for more than two decades. Despite their wide availability, trainee engagement with these tools to accumulate and synthesize the vast amount of information required to succeed in radiology remains challenging. Knowledge gaps exist among trainees because of natural differences in learning styles, study habits, and case exposure at the reading station. Further, current pressures in academic radiology limit the availability of workstation teaching, which is the most effective teaching and learning strategy in the field. Leading educators aim to bridge these gaps both by leveraging artificial intelligence (AI) large language models (LLM) to develop precision learning curricula personalized to the trainee and by creating gamified learning tools to boost the engagement of trainees. Gamification can also be used to simulate learning experiences for trainees. These tools show promise based on user feedback, both by educators and learners. Further, the synergy of AI and gamification strategies may help address knowledge gaps precisely and efficiently. We explore some of these emerging tools, their application, potential benefits, and limitations within radiology with a focus on breast imaging training.<\/jats:p>","DOI":"10.1093\/jbi\/wbag060","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:48:27Z","timestamp":1792601307000},"source":"Crossref","is-referenced-by-count":0,"title":["Innovative Learning Tools in Breast Imaging"],"prefix":"10.1093","author":[{"given":"Benjamin","family":"Vega","sequence":"first","affiliation":[{"name":"University of Missouri-Columbia School of Medicine, Department of Radiology , Columbia, MO ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2311-7442","authenticated-orcid":false,"given":"Yiming","family":"Gao","sequence":"additional","affiliation":[{"name":"New York University Grossman School of Medicine, Department of Radiology , New York, NY ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"issue":"10","key":"2026102112482339000_wbag060-B1","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1016\/s1076-6332(03)80518-4","article-title":"Development and Evaluation of an Interactive Web-Based Breast Imaging Game for Medical Students","volume":"9","author":"Roubidoux","year":"2002","journal-title":"Acad Radiol"},{"key":"2026102112482339000_wbag060-B2","author":"Vega"},{"issue":"5","key":"2026102112482339000_wbag060-B3","doi-asserted-by":"publisher","first-page":"907","DOI":"10.2214\/AJR.15.15020","article-title":"Radiology Resident\u2019 Satisfaction With Their Training and Education in the United States: Effect of Program Directors, Teaching Faculty, and Other Factors on Program Success","volume":"206","author":"Lam","year":"2016","journal-title":"AJR Am J Roentgenol"},{"issue":"7","key":"2026102112482339000_wbag060-B4","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1016\/j.jacr.2021.02.022","article-title":"COVID-19 and Breast Radiologist Wellness: Impact of Gender, Financial Loss, and Childcare Need","volume":"18","author":"Milch","year":"2021","journal-title":"J Am Coll Radiol"},{"issue":"1","key":"2026102112482339000_wbag060-B5","doi-asserted-by":"publisher","first-page":"e2432504","DOI":"10.2214\/AJR.24.32504","article-title":"Artificial Intelligence and Precision Education: AJR Podcast Series on Training and Education, Episode 7","volume":"224","author":"Cheng","year":"2025","journal-title":"AJR Am J Roentgenol"},{"issue":"11","key":"2026102112482339000_wbag060-B6","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1016\/j.jacr.2025.07.004","article-title":"Artificial Intelligence and Its Effect on Radiology Residency Education: Current Challenges, Opportunities, and Future Directions","volume":"22","author":"Volin","year":"2025","journal-title":"J Am Coll Radiol"},{"issue":"10","key":"2026102112482339000_wbag060-B7","doi-asserted-by":"publisher","first-page":"5525","DOI":"10.1007\/s00330-020-06946-y","article-title":"Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors","volume":"30","author":"Strohm L, Hehakaya C, Ranschaert ER, Boon WPC, Moors EHM","year":"2020","journal-title":"Eur Radiol"},{"key":"2026102112482339000_wbag060-B8","doi-asserted-by":"publisher","first-page":"111798","DOI":"10.1016\/j.ejrad.2024.111798","article-title":"AI in radiology: From promise to practice \u2212 A guide to effective integration","volume":"181","author":"Katal","year":"2024","journal-title":"Eur J Radiol"},{"issue":"1","key":"2026102112482339000_wbag060-B9","doi-asserted-by":"publisher","first-page":"e220084","DOI":"10.1148\/ryai.220084","article-title":"Artificial Intelligence and Radiology Education","volume":"5","author":"Tejani","year":"2022","journal-title":"Radiol Artif Intell"},{"key":"2026102112482339000_wbag060-B10","doi-asserted-by":"publisher","first-page":"11491","DOI":"10.15766\/mep_2374-8265.11491","volume":"21","author":"Grellinger","year":"2025","journal-title":"BINGO! 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