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      Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence

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          Abstract

          With the exponential advancement of artificial intelligence (AI) technology, the realm of medicine is experiencing a paradigm shift, engendering a multitude of prospects and trials for healthcare practitioners, encompassing those devoted to the practice of traditional Chinese medicine (TCM). This study explores the evolving landscape for TCM practitioners in the AI era, emphasizing that while AI can be helpful, it cannot replace the role of TCM practitioners. It is paramount to underscore the intrinsic worth of human expertise, accentuating that artificial intelligence (AI) is merely an instrument. On the one hand, AI-enabled tools like intelligent symptom checkers, diagnostic assistance systems, and personalized treatment plans can augment TCM practitioners’ expertise and capacity, improving diagnosis accuracy and treatment efficacy. AI-empowered collaborations between Western medicine and TCM can strengthen holistic care. On the other hand, AI may disrupt conventional TCM workflow and doctor-patient relationships. Maintaining the humanistic spirit of TCM while embracing AI requires upholding professional ethics and establishing appropriate regulations. To leverage AI while retaining the essence of TCM, practitioners need to hone holistic analytical skills and see AI as complementary. By highlighting promising applications and potential risks of AI in TCM, this study provides strategic insights for stakeholders to promote the integrated development of AI and TCM for better patient outcomes. With proper implementation, AI can become a valuable assistant for TCM practitioners to elevate healthcare quality.

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          Most cited references39

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              Feasibility of using deep learning to detect coronary artery disease based on facial photo

              Facial features were associated with increased risk of coronary artery disease (CAD). We developed and validated a deep learning algorithm for detecting CAD based on facial photos. We conducted a multicentre cross-sectional study of patients undergoing coronary angiography or computed tomography angiography at nine Chinese sites to train and validate a deep convolutional neural network for the detection of CAD (at least one ≥50% stenosis) from patient facial photos. Between July 2017 and March 2019, 5796 patients from eight sites were consecutively enrolled and randomly divided into training (90%, n = 5216) and validation (10%, n = 580) groups for algorithm development. Between April 2019 and July 2019, 1013 patients from nine sites were enrolled in test group for algorithm test. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using radiologist diagnosis as the reference standard. Using an operating cut point with high sensitivity, the CAD detection algorithm had sensitivity of 0.80 and specificity of 0.54 in the test group; the AUC was 0.730 (95% confidence interval, 0.699–0.761). The AUC for the algorithm was higher than that for the Diamond–Forrester model (0.730 vs. 0.623, P < 0.001) and the CAD consortium clinical score (0.730 vs. 0.652, P < 0.001). Our results suggested that a deep learning algorithm based on facial photos can assist in CAD detection in this Chinese cohort. This technique may hold promise for pre-test CAD probability assessment in outpatient clinics or CAD screening in community. Further studies to develop a clinical available tool are warranted.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2576923/overviewRole: Role: Role:
                URI : https://loop.frontiersin.org/people/2196836/overviewRole: Role:
                Role: Role: Role:
                Role: Role:
                URI : https://loop.frontiersin.org/people/2122664/overviewRole: Role: Role:
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                Journal
                Front Med (Lausanne)
                Front Med (Lausanne)
                Front. Med.
                Frontiers in Medicine
                Frontiers Media S.A.
                2296-858X
                11 January 2024
                2023
                : 10
                : 1336175
                Affiliations
                [1] 1School of Marxism, Capital Normal University , Beijing, China
                [2] 2Wangjing Hospital of China Academy of Traditional Chinese Medicine , Beijing, China
                [3] 3Graduate School of Chinese Academy of Traditional Chinese Medicine , Beijing, China
                [4] 4China Academy of Chinese Medical Sciences , Beijing, China
                Author notes

                Edited by: Filippo Gibelli, University of Camerino, Italy

                Reviewed by: Massimo Montisci, University of Padova, Italy

                *Correspondence: Lili Xu, 772032533@ 123456qq.com
                Article
                10.3389/fmed.2023.1336175
                10808796
                38274445
                2bac2e66-ad06-4594-a5ab-b0f8874d6a9b
                Copyright © 2024 Li, Ge, Liu, Xu, Zhai and Yu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 13 November 2023
                : 27 December 2023
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 40, Pages: 6, Words: 4778
                Funding
                The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
                Categories
                Medicine
                Perspective
                Custom metadata
                Regulatory Science

                artificial intelligence,traditional chinese medicine,chinese medicine doctor,opportunities,challenges

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