Artificial Intelligence: Guidance for clinical imaging and therapeutic radiography professionals, a summary by the Society of Radiographers AI working group.
| Title: | Artificial Intelligence: Guidance for clinical imaging and therapeutic radiography professionals, a summary by the Society of Radiographers AI working group. |
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| Authors: | Malamateniou C; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, City, University of London, Northampton Square, London, EC1V 0HB, UK; Perinatal Imaging and Health, King's College, London, UK. Electronic address: christina.malamateniou@city.ac.uk.; McFadden S; School of Health Sciences, Ulster University, Belfast, Northern Ireland, BT37OQB, UK.; McQuinlan Y; Mirada Medical, UK; Honorary Dosimetrist, Guy's and St Thomas' NHS Trust, UK.; England A; School of Allied Health Professions, Keele University, Staffordshire, UK.; Woznitza N; Radiology Department, University College London Hospitals, UK; School of Allied and Public Health Professions Canterbury Christ Church University, UK.; Goldsworthy S; Beacon Radiotherapy, Musgrove Park Hospital, Somerset NHS Foundation Trust, Taunton, TA1 5DA, UK.; Currie C; Programme Lead MSc Diagnostic Imaging, Glasgow Caledonian University, UK; MRI Specialist Radiographer, Queen Elizabeth University Hospital, Glasgow, UK.; Skelton E; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, City, University of London, Northampton Square, London, EC1V 0HB, UK; Perinatal Imaging and Health, King's College, London, UK.; Chu KY; Department of Oncology, University of Oxford, UK; Radiotherapy Department, Oxford University Hospitals, NHS FT, UK.; Alware N; King George Hospital, BHRUT NHS Trust, London, UK.; Matthews P; Diagnostic Imaging Department, Surrey & Sussex Healthcare NHS Trust, UK.; Hawkesford R; SABR Radiotherapy Lead at UHCW Coventry, UK.; Tucker R; School of Allied Health and Social Care, College of Health, Psychology and Social Care, University of Derby, UK; Radiology Department, Nottingham University Hospital NHS Trust, UK.; Town W; Dartford and Gravesham NHS Trust, UK.; Matthew J; Department of Radiography, Division of Midwifery and Radiography, School of Health Sciences, City, University of London, Northampton Square, London, EC1V 0HB, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, SE1 7EH, UK.; Kalinka C; Society and College of Radiographers, UK; Programme Manager, Strategic Programme Unit, NHS Collaborative, Wales, United Kingdom.; O'Regan T; The Society and College of Radiographers, 207 Providence Square, Mill Street, London, UK. |
| Source: | Radiography (London, England : 1995) [Radiography (Lond)] 2021 Nov; Vol. 27 (4), pp. 1192-1202. Date of Electronic Publication: 2021 Aug 20. |
| Publication Type: | Journal Article; Review |
| Language: | English |
| Journal Info: | Publisher: Elsevier B.V Country of Publication: Netherlands NLM ID: 9604102 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-2831 (Electronic) Linking ISSN: 10788174 NLM ISO Abbreviation: Radiography (Lond) Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2003- : Amsterdam : Elsevier B.V.; Original Publication: London ; Philadelphia : W.B. Saunders Co., c1995- |
| MeSH Terms: | Artificial Intelligence* ; Radiology*; Allied Health Personnel ; Ecosystem ; Humans ; Radiography |
| Abstract: | Introduction: Artificial intelligence (AI) has started to be increasingly adopted in medical imaging and radiotherapy clinical practice, however research, education and partnerships have not really caught up yet to facilitate a safe and effective transition. The aim of the document is to provide baseline guidance for radiographers working in the field of AI in education, research, clinical practice and stakeholder partnerships. The guideline is intended for use by the multi-professional clinical imaging and radiotherapy teams, including all staff, volunteers, students and learners.; Methods: The format mirrored similar publications from other SCoR working groups in the past. The recommendations have been subject to a rapid period of peer, professional and patient assessment and review. Feedback was sought from a range of SoR members and advisory groups, as well as from the SoR director of professional policy, as well as from external experts. Amendments were then made in line with feedback received and a final consensus was reached.; Results: AI is an innovative tool radiographers will need to engage with to ensure a safe and efficient clinical service in imaging and radiotherapy. Educational provisions will need to be proportionately adjusted by Higher Education Institutions (HEIs) to offer the necessary knowledge, skills and competences for diagnostic and therapeutic radiographers, to enable them to navigate a future where AI will be central to patient diagnosis and treatment pathways. Radiography-led research in AI should address key clinical challenges and enable radiographers co-design, implement and validate AI solutions. Partnerships are key in ensuring the contribution of radiographers is integrated into healthcare AI ecosystems for the benefit of the patients and service users.; Conclusion: Radiography is starting to work towards a future with AI-enabled healthcare. This guidance offers some recommendations for different areas of radiography practice. There is a need to update our educational curricula, rethink our research priorities, forge new strong clinical-academic-industry partnerships to optimise clinical practice. Specific recommendations in relation to clinical practice, education, research and the forging of partnerships with key stakeholders are discussed, with potential impact on policy and practice in all these domains. These recommendations aim to serve as baseline guidance for UK radiographers.; Implications for Practice: This review offers the most up-to-date recommendations for clinical practitioners, researchers, academics and service users of clinical imaging and therapeutic radiography services. Radiography practice, education and research must gradually adjust to AI-enabled healthcare systems to ensure gains of AI technologies are maximised and challenges and risks are minimised. This guidance will need to be updated regularly given the fast-changing pace of AI development and innovation.; (Crown Copyright © 2021. Published by Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Conflict of interest statement Unrelated to the current submission, Dr Nicholas Woznitza is a clinical advisor to InHealth and clinical director of Health Education England London radiographer reporting programme. Ms Yasmin McQuinlan is an employee of Mirada Medical. This work was done in her personal time and neither herself or the company received any reimbursement or benefits as a result. Dr Christina Malamateniou is the programme director of the Introduction to AI for radiographers module, that is mentioned in this publication. No external funding was sought to assist with guidance development. All working group members gave their time and expertise voluntarily. The SCoR professional officer was a member of staff in a full-time role, spending time on the working party as SCoR time and resource allowed. |
| Contributed Indexing: | Keywords: Artificial intelligence; Guidance; Machine learning; Radiographer; Recommendations |
| Entry Date(s): | Date Created: 20210823 Date Completed: 20211026 Latest Revision: 20211026 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.radi.2021.07.028 |
| PMID: | 34420888 |
| Database: | MEDLINE |
Journal Article; Review