Susan C Shelmerdine1,2,3,4, Karen Rosendahl5,6, Owen J Arthurs7,8,9. 1. Department of Clinical Radiology, Great Ormond Street Hospital for Children, London, WC1N 3JH, UK. susan.shelmerdine@gosh.nhs.uk. 2. UCL Great Ormond Street Institute of Child Health, Great Ormond Street Hospital for Children, London, UK. susan.shelmerdine@gosh.nhs.uk. 3. Great Ormond Street Hospital NIHR Biomedical Research Centre, London, UK. susan.shelmerdine@gosh.nhs.uk. 4. Department of Clinical Radiology, St. George's Hospital, London, UK. susan.shelmerdine@gosh.nhs.uk. 5. Department of Clinical Medicine, UiT The Arctic University of Norway, Tromsø, Norway. 6. Department of Radiology, University Hospital of North Norway, Tromsø, Norway. 7. Department of Clinical Radiology, Great Ormond Street Hospital for Children, London, WC1N 3JH, UK. 8. UCL Great Ormond Street Institute of Child Health, Great Ormond Street Hospital for Children, London, UK. 9. Great Ormond Street Hospital NIHR Biomedical Research Centre, London, UK.
Abstract
BACKGROUND: The nature of paediatric radiology work poses several challenges for developing and implementing artificial intelligence (AI) tools, but opinions of those working in the field are currently unknown. OBJECTIVE: To evaluate the attitudes and perceptions toward AI amongst health care professionals working within children's imaging services. MATERIALS AND METHODS: A web-based questionnaire was distributed to the membership of several paediatric and general radiological societies over a 4-month period (1 Feb - 31 May 2020). Survey questions covered attitudes toward AI in general, future impacts and suggested areas for development specifically within paediatric imaging. RESULTS: Two hundred and forty responses were collected with the majority being from radiologists (159/240, 66.3%; 95% confidence interval [CI] 59.8-72.2%) or allied health care professionals (72/240, 31.3%; 95% CI 25.4-37.5%). Respondents agreed that AI could potentially alert radiologists to imaging abnormalities (148/240, 61.7%; 95% CI 55.2-67.9%) but preferred that results were checked by a human (200/240, 83.3%; 95% CI 78.0-87.8%). The majority did not believe jobs in paediatric radiology would be replaced by AI (205/240, 85.4%; 95% CI 80.3-89.6%) and that the development of AI tools should focus on improved diagnostic accuracy (77/240, 32.1%; 95% CI 26.2-38.4%), workflow efficiencies (60/240, 25.0%; 95% CI 19.7-30.9%) and patient safety (54/240, 22.5%; 95% CI 17.4-28.3%). The majority of European Society of Paediatric Radiology (ESPR) members (67/81, 82.7%; 95% CI 72.7-90.2%) welcomed the idea of a dedicated paediatric radiology AI task force with emphasis on educational events and anonymised dataset curation. CONCLUSION: Imaging health care professionals working with children had a positive outlook regarding the use of AI in paediatric radiology, and did not feel their jobs were threatened. Future AI tools would be most beneficial for easily automated tasks and most practitioners welcomed the opportunity for further AI educational activities.
BACKGROUND: The nature of paediatric radiology work poses several challenges for developing and implementing artificial intelligence (AI) tools, but opinions of those working in the field are currently unknown. OBJECTIVE: To evaluate the attitudes and perceptions toward AI amongst health care professionals working within children's imaging services. MATERIALS AND METHODS: A web-based questionnaire was distributed to the membership of several paediatric and general radiological societies over a 4-month period (1 Feb - 31 May 2020). Survey questions covered attitudes toward AI in general, future impacts and suggested areas for development specifically within paediatric imaging. RESULTS: Two hundred and forty responses were collected with the majority being from radiologists (159/240, 66.3%; 95% confidence interval [CI] 59.8-72.2%) or allied health care professionals (72/240, 31.3%; 95% CI 25.4-37.5%). Respondents agreed that AI could potentially alert radiologists to imaging abnormalities (148/240, 61.7%; 95% CI 55.2-67.9%) but preferred that results were checked by a human (200/240, 83.3%; 95% CI 78.0-87.8%). The majority did not believe jobs in paediatric radiology would be replaced by AI (205/240, 85.4%; 95% CI 80.3-89.6%) and that the development of AI tools should focus on improved diagnostic accuracy (77/240, 32.1%; 95% CI 26.2-38.4%), workflow efficiencies (60/240, 25.0%; 95% CI 19.7-30.9%) and patient safety (54/240, 22.5%; 95% CI 17.4-28.3%). The majority of European Society of Paediatric Radiology (ESPR) members (67/81, 82.7%; 95% CI 72.7-90.2%) welcomed the idea of a dedicated paediatric radiology AI task force with emphasis on educational events and anonymised dataset curation. CONCLUSION: Imaging health care professionals working with children had a positive outlook regarding the use of AI in paediatric radiology, and did not feel their jobs were threatened. Future AI tools would be most beneficial for easily automated tasks and most practitioners welcomed the opportunity for further AI educational activities.
Authors: Lene Bjerke Laborie; Jaishree Naidoo; Erika Pace; Pierluigi Ciet; Christine Eade; Matthias W Wagner; Thierry A G M Huisman; Susan C Shelmerdine Journal: Pediatr Radiol Date: 2022-06-22
Authors: Mingyang Chen; Bo Zhang; Ziting Cai; Samuel Seery; Maria J Gonzalez; Nasra M Ali; Ran Ren; Youlin Qiao; Peng Xue; Yu Jiang Journal: Front Med (Lausanne) Date: 2022-08-31