Physician sentiment toward artificial intelligence (AI) in colonoscopic practice: a survey of US gastroenterologists
Vaibhav Wadhwaa, Muthuraman Alagappana, Adalberto Jose Gonzalezb, Kapil Guptac, Jeremy R. Glissen Browna, Jonah Cohena, Mandeep Sawhneya, Douglas K. Pleskowa, Tyler M. Berzina
- aDepartment of Gastroenterology and Hepatology, Cleveland Clinic Florida, Weston, Florida, United States
- bCenter for Advanced Endoscopy, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts, United States
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Abstract
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.