Non‐radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports. Issue 8 (21st February 2022)

• Record Type: Journal Article
• Authors: Lim, Sophie Soyeon; Phan, Tuan D; Law, Meng; Goh, Gerard S; Moriarty, Heather K; Lukies, Matthew W; Joseph, Timothy; Clements, Warren
• Is Part Of: Journal of medical imaging and radiation oncology. Volume 66:Issue 8(2022)
• Page Start: 1029
• Page End: 1034
• Publication Date: 2022-02-21
• Subjects: AI -- artificial intelligence -- radiology -- report -- survey; Radiology, Medical -- Periodicals; Radiology, Medical -- Australasia -- Periodicals; 616.0757
• DOI: 10.1111/1754-9485.13388
• Languages: English
• ISSNs: 1754-9477

Abstract

Abstract: Introduction: Incorporating artificial intelligence (AI) in diagnostic medical imaging reports has the potential to improve efficiency. Although perception of radiologists, radiographers, medical students and patients on AI use in image reporting has been explored, there is limited literature on non‐radiologist clinicians' opinion on this topic. Method: Single‐centre online survey targeting non‐radiologist medical staff conducted from May to August 2021 at a tertiary referral hospital in Melbourne, Australia. Survey questions revolved around clinicians' level of comfort acting on AI‐generated reports with varying levels of radiologist involvement and scan complexity, opinion on medicolegal responsibility for erroneous AI‐issued reports and perception of data privacy and security. Results: Eighty‐eight responses were collected, including 47.9% of consultants. Non‐radiologist clinicians across all seniorities and specialties felt significantly less comfortable acting on AI‐issued reports compared with radiologist‐issued reports (mean comfort radiologist 6.44/7, mean comfort AI 3.35/7, P < 0.001) but felt equally comfortable with an AI‐hybrid model of care (mean comfort hybrid 6.38/7, P = 0.676). Non‐radiologist clinicians believed that medicolegal responsibility with errors in AI‐issued reports mostly lay with hospitals or health service providers (65.9%) and radiologists (54.5%). Regarding data privacy and security, non‐radiologist clinicians felt significantly less comfortable with AI issuing image reports instead of radiologists ( P < 0.001). Conclusion: A hybrid AI‐generated radiologist‐confirmed method of image reporting may be the ideal way of integrating AI into clinical practice based on the perception of our referring non‐radiologist medical colleagues. Formal guidelines on medicolegal responsibility and data privacy should be established prior to utilising AI in the clinical setting.