Radiographer/radiologist education and learning in artificial intelligence (REAL-AI)
| dc.contributor.author | Doherty, Geraldine | en |
| dc.contributor.author | McLaughlin, Laura | en |
| dc.contributor.author | Rainey, Clare | en |
| dc.contributor.author | Hughes, Ciara | en |
| dc.contributor.author | Bond, Raymond | en |
| dc.contributor.author | McConnell, Jonathan | en |
| dc.contributor.author | McFadden, Sonyia L. | en |
| dc.date.accessioned | 2025-01-27T12:36:08Z | |
| dc.date.available | 2025-01-27T12:36:08Z | |
| dc.date.issued | 2023 | en |
| dc.description.abstract | Background: Artificial intelligence (AI) is incipient in radiography, and whilst there are many studies investigating its potential in the clinical environment, there is a paucity of research investigating the needs of clinical staff. Further research is required to identify what training and preparation is required for a new AI-powered work environment, or indeed what AI education is available at undergraduate and postgraduate levels. Method: This CoRIPS funded study included two electronic surveys (i) one was performed amongst radiographers and radiologists investigating their baseline AI knowledge, identifying what training they desire and preferred method of delivery. (ii) the second survey was for academics and educators in Higher Education Institutions to identify educational provision of AI in the radiography curriculum across the UK and Europe. Method: Data collection and analysis are underway and will be completed at the European Congress of Radiology in Vienna, March 2023. Participant feedback will determine perceptions of clinical staff and identify topics for inclusion in postgraduate/undergraduate programmes. Method: will inform the next phase of the study which will incorporate focus groups with staff to explore adaptation of the curricula to enable incorporation of AI into clinical practice. Conclusion: Radiographers, radiologists and Higher Education Institutions have been surveyed to ascertain current knowledge and needs for AI training. Collaboration and symbiosis between academia, clinical and industry partners is possible, to pioneer AI education tailored to medical imaging staff. The impact of this research has the potential to be of significant value across disciplines within the wider healthcare sector. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Published Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Doherty, G., Mc Laughlin, L., Rainey, C., Hughes, C., Bond, R.R., McConnell, J., & McFadden, S. (2023), 'Radiographer/radiologist education and learning in artificial intelligence (REAL-AI)', UK Imaging & Oncology Congress 2023, Liverpool, United Kingdom, 5 -7 June. | en |
| dc.identifier.uri | https://hdl.handle.net/10468/16898 | |
| dc.language.iso | en | en |
| dc.publisher | ROC Events | en |
| dc.rights | © 2023, ROC Events Ltd. | en |
| dc.subject | Radiographer/radiologist education | en |
| dc.subject | Needs for AI training | en |
| dc.title | Radiographer/radiologist education and learning in artificial intelligence (REAL-AI) | en |
| dc.type | Conference item | en |
