Radiographer education and learning in artificial intelligence (REAL_AI): Findings from an online educational intervention

Research output: Contribution to conferenceOther

Abstract

Background: Artificial intelligence (AI) is incipient in medical imaging and is significantly impacting the field. Despite the rapid implementation of AI tools to clinical practice, there remains a paucity of research to investigate the educational needs of staff who are expected to effectively utilise this technology. Earlier stages of the REAL_AI project gathered data on these needs and requirements, which was used to curate an educational intervention aimed at enhancing the AI awareness of medical imaging staff. AI readiness has been identified as a precursor to AI adoption, and education is a tool to combat fear and resistance which is a barrier to adoption.
Methods: An e-learning educational module tailored to healthcare professionals was designed and delivered using NHS England’s E-Learning for Health (eLfH) portal in the UK. Results from the initial phases of the REAL_AI study were used to inform and curate a beginner’s introductory module on AI, covering a range of topics including basic terminology, applications of AI in healthcare, types of AI, and ethical concerns. To assess impact and evaluate overall effectiveness of the intervention, short pre- and post- surveys were designed to capture basic demographics and identify participants’ awareness level of AI applications in medical imaging, and their level of confidence with, trust in, and willingness to use AI. eLfH also captured usage data and engagement metrics. Surveys included a mixture of Likert-scale, multiple choice and open-ended questions. Data collection is ongoing by eLfH as participants access the portal and anonymised prior to being returned. Analysis of the complete dataset will be performed using SPSS and N-VIVO, to identify any thematic or quantitative indications of the module’s impact.
Results: Preliminary results identify users from a range of healthcare backgrounds, including allied heath, medical and dental, and nursing and midwifery. A total of 127 participants completed the pre-intervention survey and 70 completed the post-intervention survey. Preliminary results indicate changes in participants’ awareness of AI applications in medical imaging. Pre-intervention, 19% (n=24) of participants indicated they felt not at all confident in using AI technologies in clinical practice, this dropped to 0% in the post-intervention survey. 10.2% (n=13) indicated they were very confident, this increased to 22.9% (n=16) post-intervention. Pre-intervention, 10.2% (n=13) indicated they would not trust the decision of an AI algorithm in clinical practice, this dropped to 2.9% in the post-intervention survey. 4.7% (n=6) indicated they would be very trusting of the AI decision pre-intervention, which increased to 10% (n=7) post engagement with the educational intervention. Complete data analysis is scheduled to begin in June 2024 and will be completed for presentation at the EuSoMII conference in October 2024.
Conclusion: The findings of this study will provide a vignette of the current awareness of AI and insight as to the impact of this targeted method of delivery. Results will inform future iterations of the eLfH module content and lay a foundation for scaling and disseminating similar interventions across the wider healthcare community.
Original languageEnglish
Publication statusAccepted/In press - 12 Oct 2024
EventEuropean Society of Medical Imaging Informatics - M.A.C.C. Meeting Art and Craft Center, Pisa, Italy
Duration: 10 Oct 202412 Oct 2024
https://www.eusomii.org/events/eusomii-annual-meeting-2024/

Conference

ConferenceEuropean Society of Medical Imaging Informatics
Abbreviated titleEuSoMII
Country/TerritoryItaly
CityPisa
Period10/10/2412/10/24
Internet address

Keywords

  • medical imaging
  • artificial intelligence (AI)
  • radiography
  • education

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