Dr. Answer AI for prostate cancer: Intention to use, expected effects, performance, and concerns of urologists

• Published in: Prostate international Vol. 10; no. 1; pp. 38 - 44
• Main Authors: Rho, Mi Jung, Park, Jihwan, Moon, Hyong Woo, Kim, Choung-Soo, Jeon, Seong Soo, Kang, Minyong, Lee, Ji Youl
• Format: Journal Article
• Language: English
• Published: Korea (South) Elsevier B.V 01.03.2022; Asian Pacific Prostate Society; Elsevier
• Subjects: Acceptance; Artificial intelligence; Doctor’s Answer; PROMISE CLIP project; Prostate cancer; Acceptance; Doctor’s Answer; Artificial intelligence; Prostate cancer; PROMISE CLIP project
• ISSN: 2287-8882; 2287-903X
• DOI: 10.1016/j.prnil.2021.09.001

To efficiently implement artificial intelligence (AI) software for medical applications, it is crucial to understand the acceptance, expected effects, expected performance, and concerns of software users. In this study, we examine the acceptance and expectation of the Dr. Answer AI software for prostate cancer. We conducted an online survey for urologists from August 13 to September 18, 2020. The target software is an AI-based clinical software called Dr. Answer AI software, used for prostate cancer diagnosis. We collected data from 86 urologists and conducted a basic statistical and multiple regression analysis using the R package. The compatibility was significantly associated with the intention to use the Dr. Answer AI software. The expected average accuracy for the software ranges from 86.91% to 87.51%, and the urologists perceived that the cloud method is suitable to introduce the software. The most desirable function of the software for the specialists is predicting the occurrence of extracapsular extension, seminal vesicle invasion, and lymph node metastasis after radical prostatectomy. Finally, the primary concerns involved the cost, compatibility with existing systems, and obtaining accurate information from the software. Our results present an understanding of the acceptance, expected effects, expected performance, and concerns of software users. The results provide a guide to help AI software be properly developed and implemented in medical applications.