Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy

Yuichi Mori; Eun Hyo Jin; Dongheon Lee

doi:10.1016/j.dld.2023.11.033

Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy

Dig Liver Dis. 2023 Dec 16:S1590-8658(23)01072-1. doi: 10.1016/j.dld.2023.11.033. Online ahead of print.

Authors

Yuichi Mori¹, Eun Hyo Jin², Dongheon Lee³

Affiliations

¹ Clinical Effectiveness Research Group, University of Oslo and Oslo University Hospital, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
² Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea; Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, South Korea. Electronic address: icetea@snuh.org.
³ Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon, South Korea; Department of Biomedical Engineering, Chungnam National University Hospital, Daejeon, South Korea.

PMID: 38105144
DOI: 10.1016/j.dld.2023.11.033

Abstract

Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integration of computer-aided diagnosis (CADx) in colonoscopy. Active learning using diverse polyp image datasets can help in developing precise CADx systems. Enhancing doctors' digital literacy and interpreting their results is crucial. Explainable artificial intelligence (AI) addresses opacity, and textual descriptions, along with AI-generated content, deepen the interpretability of AI-based findings by doctors. AI conveying uncertainties and decision confidence aids doctors' acceptance of results. Optimal AI-doctor collaboration requires improving algorithm performance, transparency, addressing uncertainties, and enhancing doctors' optical diagnostic skills.

Keywords: Algorithms; Confidence; Precision; Trust.