{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:02:38Z","timestamp":1790226158037,"version":"4.1.0"},"reference-count":13,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is influencing diagnostic medical imaging practice and education. Despite this, little is known about AI use in education and practice in sub-Saharan Africa. As a first step towards informing future curriculum development, we assessed AI awareness, self-reported familiarity, and attitudes among Ghanaian diagnostic medical imaging students.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This cross-sectional institution-based study in Ghana used a self-administered 21-item questionnaire to collect data from diagnostic medical imaging students (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009296) of the University of Cape Coast (UCC), University for Development Studies (UDS), Kwame Nkrumah University of Science and Technology (KNUST), University of Health and Allied Sciences (UHAS), and University of Ghana (UG) from June to August 2024. Binary logistic regression was used to examine factors associated with support for including AI as a core component of the medical imaging curriculum.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Nearly all respondents (95.3%) were aware of AI, and 85.8% had ever used an AI product. Most participants also considered AI an emerging trend in medical imaging (85.8%) and agreed that it has a net positive impact on medical imaging (76.0%). Seventy-six percent of participants supported the inclusion of AI in core medical imaging courses. In multivariable logistic regression, male gender (OR\u2009=\u20091.94, 95% CI: 1.07\u20133.51,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.030) and agreement that AI will positively affect medical imaging (OR\u2009=\u20092.99, 95% CI: 1.56\u20135.73,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.001) were associated with support for AI curriculum integration after adjustment. UDS enrolment showed a weak association with curriculum endorsement (OR\u2009=\u20092.45, 95% CI: 1.00\u20135.97,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.049). Academic level and perceived job threat were not independently associated with endorsement of AI curriculum inclusion.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Ghanaian diagnostic medical imaging students demonstrated high awareness of AI, substantial self-reported exposure to AI-enabled products or services, and generally positive attitudes toward its potential applications in medical imaging. A positive perception of AI's overall impact was associated with greater support for formal AI education. These findings indicate strong student receptiveness to AI education but should not be interpreted as evidence of objective AI knowledge or competency.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1903859","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T04:39:30Z","timestamp":1790224770000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Awareness and attitudes regarding artificial intelligence in medical imaging: student perspectives from five Ghanaian universities"],"prefix":"10.3389","volume":"13","author":[{"given":"Issah Zabsonre","family":"Alhassan","sequence":"first","affiliation":[{"name":"Department of Medical Imaging, School of Allied Health Sciences, University for Development Studies","place":["Tamale, Ghana"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul-Salam","family":"Issahaku","sequence":"additional","affiliation":[{"name":"Department of Applied Physics, School of Physical Sciences, University of Technology and Applied Sciences","place":["Navrongo, Ghana"]},{"name":"Department of Medical Physics, School of Nuclear and Allied Sciences, University of Ghana","place":["Accra, Ghana"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatahia","family":"Alidu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, School of Allied Health Sciences, University for Development Studies","place":["Tamale, Ghana"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed Rashad Suhuyini","family":"Abdul-Bach","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, School of Allied Health Sciences, University for Development Studies","place":["Tamale, Ghana"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat Rev Cancer"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e1002686","DOI":"10.1371\/journal.pmed.1002686","article-title":"Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists","volume":"15","author":"Rajpurkar","year":"2018","journal-title":"PLoS Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/s13244-023-01372-2","article-title":"Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study","volume":"14","author":"van 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Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1903859\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T04:39:44Z","timestamp":1790224784000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1903859\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":13,"alternative-id":["10.3389\/fmed.2026.1903859"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1903859","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]},"article-number":"1903859"}}