Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment

• Published in: Frontiers in oncology Vol. 13; p. 1152013
• Main Authors: Han, Chunguang, Pan, Yubo, Liu, Chang, Yang, Xiaowei, Li, Jianbin, Wang, Kun, Sun, Zhengkui, Liu, Hui, Jin, Gongsheng, Fang, Fang, Pan, Xiaofeng, Tang, Tong, Chen, Xiao, Pang, Shiyong, Ma, Li, Wang, Xiaodong, Ren, Yun, Liu, Mengyou, Liu, Feng, Jiang, Mengxue, Zhao, Jiqi, Lu, Chenyang, Lu, Zhengdong, Gao, Dongjing, Jiang, Zefei, Pei, Jing
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 09.06.2023
• Subjects: artificial intelligence; breast cancer; clinical decision support system; CSCO AI; decision-making quality; Oncology; clinical decision support system; breast cancer; CSCO AI; decision-making quality; artificial intelligence
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2023.1152013

AI-based clinical decision support system (CDSS) has important prospects in overcoming the current informational challenges that cancer diseases faced, promoting the homogeneous development of standardized treatment among different geographical regions, and reforming the medical model. However, there are still a lack of relevant indicators to comprehensively assess its decision-making quality and clinical impact, which greatly limits the development of its clinical research and clinical application. This study aims to develop and application an assessment system that can comprehensively assess the decision-making quality and clinical impacts of physicians and CDSS. Enrolled adjuvant treatment decision stage early breast cancer cases were randomly assigned to different decision-making physician panels (each panel consisted of three different seniority physicians in different grades hospitals), each physician made an independent "Initial Decision" and then reviewed the CDSS report online and made a "Final Decision". In addition, the CDSS and guideline expert groups independently review all cases and generate "CDSS Recommendations" and "Guideline Recommendations" respectively. Based on the design framework, a multi-level multi-indicator system including "Decision Concordance", "Calibrated Concordance", " Decision Concordance with High-level Physician", "Consensus Rate", "Decision Stability", "Guideline Conformity", and "Calibrated Conformity" were constructed. 531 cases containing 2124 decision points were enrolled; 27 different seniority physicians from 10 different grades hospitals have generated 6372 decision opinions before and after referring to the "CDSS Recommendations" report respectively. Overall, the calibrated decision concordance was significantly higher for CDSS and provincial-senior physicians (80.9%) than other physicians. At the same time, CDSS has a higher " decision concordance with high-level physician" (76.3%-91.5%) than all physicians. The CDSS had significantly higher guideline conformity than all decision-making physicians and less internal variation, with an overall guideline conformity variance of 17.5% (97.5% vs. 80.0%), a standard deviation variance of 6.6% (1.3% vs. 7.9%), and a mean difference variance of 7.8% (1.5% vs. 9.3%). In addition, provincial-middle seniority physicians had the highest decision stability (54.5%). The overall consensus rate among physicians was 64.2%. There are significant internal variation in the standardization treatment level of different seniority physicians in different geographical regions in the adjuvant treatment of early breast cancer. CDSS has a higher standardization treatment level than all physicians and has the potential to provide immediate decision support to physicians and have a positive impact on standardizing physicians' treatment behaviors.