Preparing Radiologic Technology Students For the Age of Artificial Intelligence.
The article focuses on preparing radiologic technology students for the integration of artificial intelligence (AI) in clinical imaging practice. It highlights the growing use of AI tools in image acquisition, dose optimization, quality control, and triage, emphasizing the need for radiologic techno...
| Publicado en: | Radiologic Technology Vol. 97; no. 5; pp. 333 - 344 |
|---|---|
| Autores principales: | , |
| Formato: | tables/charts Journal Article |
| Publicado: |
American Society of Radiologic Technologists
May/Jun2026
|
| 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=195527043&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195527043 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00338397 39J jtl: Radiologic Technology issn: 00338397 maglogo: N pubinfo: dt: May/Jun2026 vid: 97 iid: 5 pid: 7052 pub: American Society of Radiologic Technologists place: Alburquerque, New Mexico artinfo: ui: 195527043 195527043 195527043 195527043 ppf: 333 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Preparing Radiologic Technology Students For the Age of Artificial Intelligence. aug: au: Stelmark, Jarek Stelmark, Raymond affil: Professor in the radiologic technology program at Hostos Community College of the City University of New York in the Bronx sug: subj: Students, Radiologic Technology Artificial Intelligence Education, Radiologic Technology Curriculum Development Needs Assessment Faculty Development Critical Thinking Informatics Quality Assurance Algorithms Collaboration Attitude to Computers Certification Computer Literacy Accreditation Strategic Planning ab: The article focuses on preparing radiologic technology students for the integration of artificial intelligence (AI) in clinical imaging practice. It highlights the growing use of AI tools in image acquisition, dose optimization, quality control, and triage, emphasizing the need for radiologic technologists to develop technical literacy, clinical judgment, and ethical awareness to safely interpret and communicate AI outputs. Despite AI’s increasing clinical presence, formal AI education remains limited in radiologic technology programs worldwide, creating a gap in readiness that parallels past technological shifts in the field. The authors propose an eight-domain AI curriculum framework addressing core concepts, ethics, clinical implementation, communication, and precision learning, supported by realistic scenarios to foster critical thinking and human oversight. They also discuss barriers such as faculty preparedness, curriculum overload, and limited clinical exposure, recommending gradual curriculum integration, faculty development programs, and accessible educational resources from professional organizations to equip students for AI-augmented workflows while maintaining patient-centered care. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|