Physician sentiment toward artificial intelligence (AI) in colonoscopic practice: a survey of US gastroenterologists

Abstract

Background and study aims Early studies have shown that artificial intelligence (AI) has the potential to augment the performance of gastroenterologists during endoscopy. Our aim was to determine how gastroenterologists view the potential role of AI in gastrointestinal endoscopy.

Methods In this cross-sectional study, an online survey was sent to US gastroenterologists. The survey included questions about physician level of training, experience, and practice characteristics and physician perception of AI. Descriptive statistics were used to summarize sentiment about AI. Univariate and multivariate analyses were used to assess whether background information about physicians correlated to their sentiment.

Results Surveys were emailed to 330 gastroenterologists nationwide. Between December 2018 and January 2019, 124 physicians (38 %) completed the survey. Eighty-six percent of physicians reported interest in AI-assisted colonoscopy; 84.7 % agreed that computer-assisted polyp detection (CADe) would improve their endoscopic performance. Of the respondents, 57.2 % felt comfortable using computer-aided diagnosis (CADx) to support a “diagnose and leave” strategy for hyperplastic polyps. Multivariate analysis showed that post-fellowship experience of fewer than 15 years was the most important factor in determining whether physicians were likely to believe that CADe would lead to more removed polyps (odds ratio = 5.09; P = .01). The most common concerns about implementation of AI were cost (75.2 %), operator dependence (62.8 %), and increased procedural time (60.3 %).

Conclusions Gastroenterologists have strong interest in the application of AI to colonoscopy, particularly with regard to CADe for polyp detection. The primary concerns were its cost, potential to increase procedural time, and potential to develop operator dependence. Future developments in AI should prioritize mitigation of these concerns.

Publication History

Received: 27 February 2020

Accepted: 18 May 2020

Article published online:
22 September 2020

• References

• 1 Ruffle JK, Farmer AD, Aziz Q. Artificial intelligence-assisted gastroenterology- promises and pitfalls. Am J Gastroenterol 2019; 114: 422-428
• 2 Ting DSW, Cheung CY, Lim G. et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017; 318: 2211-2223
• 3 Esteva A, Kuprel B, Novoa RA. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-118
• 4 Togashi K. Applications of artificial intelligence to endoscopy practice: The view from Japan Digestive Disease Week 2018. Digest Endosc 2019; 31: 270-272
• 5 Cohen J, Desilets DJ, Hwang JH. et al. Gastrointestinal Endoscopy Editorial Board top 10 topics: advances in gastrointestinal endoscopy in 2018. Gastrointest Endosc 2019; 90: 35-43
• 6 Misawa M, Kudo SE, Mori Y. et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 2018; 154: 2027-2029.e2023
• 7 Urban G, Tripathi P, Alkayali T. et al. Deep Learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 2018; 155: 1069-1078.e1068
• 8 Mori Y, Kudo SE, Misawa M. et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Annals Internal Med 2018; 169: 357-366
• 9 Chen PJ, Lin MC, Lai MJ. et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology 2018; 154: 568-575
• 10 Alagappan M, Brown JRG, Mori Y. et al. Artificial intelligence in gastrointestinal endoscopy: The future is almost here. World J Gastrointest Endosc 2018; 10: 239-249
• 11 Sarwar S, Dent A, Faust K. et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPG Digit Med 2019; 2: 28
• 12 Rex DK, Bond JH, Winawer S. et al. Quality in the technical performance of colonoscopy and the continuous quality improvement process for colonoscopy: recommendations of the U. S. Multi-Society Task Force on Colorectal Cancer. Am J Gastroenterol 2002; 97: 1296-1308
• 13 Rex DK, Petrini JL, Baron TH. et al. Quality indicators for colonoscopy. Gastrointest Endosc 2006; 63: S16-S28
• 14 Kochhar G, Wallace MB. Virtual histology in everyday gastrointestinal endoscopy. Clin Gastroenterol Hepatol 2018; 16: 1556-1561
• 15 Abu Dayyeh BK, Thosani N, Konda V. et al. ASGE Technology Committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc 2015; 81: 502.e501-502.e516
• 16 Willems P, Orkut S, Ditisheim S. et al. A239 Clinical management of colorectal polyps: results of an international survey. J Can Assoc Gastroenterol 2019; 2: 467-469
• 17 van Doorn SC, van der Vlugt M, Depla A. et al. Adenoma detection with Endocuff colonoscopy versus conventional colonoscopy: a multicentre randomised controlled trial. Gut 2017; 66: 438-445
• 18 Hassan C, Senore C, Manes G. et al. Diagnostic yield and miss rate of EndoRings in an organized colorectal cancer screening program: the SMART (Study Methodology for ADR-Related Technology) trial. Gastrointest Endosc 2019; 89: 583-590.e581
• 19 Vinsard DG, Mori Y, Misawa M. et al. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc 2019; 1: 55-63