This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1407) Clinical Information and Decision Making (1407) Innovations and Technology in Cancer Care (423)

Published on in Vol 22, No 2 (2020): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14122, first published .
Concordance Between Watson for Oncology and a Multidisciplinary Clinical Decision-Making Team for Gastric Cancer and the Prognostic Implications: Retrospective Study

Concordance Between Watson for Oncology and a Multidisciplinary Clinical Decision-Making Team for Gastric Cancer and the Prognostic Implications: Retrospective Study

Concordance Between Watson for Oncology and a Multidisciplinary Clinical Decision-Making Team for Gastric Cancer and the Prognostic Implications: Retrospective Study

Authors of this article:

Yulong Tian1 Author Orcid Image ;   Xiaodong Liu1 Author Orcid Image ;   Zixuan Wang2 Author Orcid Image ;   Shougen Cao1 Author Orcid Image ;   Zimin Liu3 Author Orcid Image ;   Qinglian Ji4 Author Orcid Image ;   Zequn Li1 Author Orcid Image ;   Yuqi Sun1 Author Orcid Image ;   Xin Zhou1 Author Orcid Image ;   Daosheng Wang1 Author Orcid Image ;   Yanbing Zhou1 Author Orcid Image
Article Authors Cited by (27) Tweetations (9) Metrics

Journals

  1. Dlamini Z, Francies F, Hull R, Marima R. Artificial intelligence (AI) and big data in cancer and precision oncology. Computational and Structural Biotechnology Journal 2020;18:2300 View
  2. Schork N, Goetz L, Lowey J, Trent J. Strategies for Testing Intervention Matching Schemes in Cancer. Clinical Pharmacology & Therapeutics 2020;108(3):542 View
  3. Bang C, Lee J, Baik G. Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy. Journal of Medical Internet Research 2020;22(9):e21983 View
  4. Aikemu B, Xue P, Hong H, Jia H, Wang C, Li S, Huang L, Ding X, Zhang H, Cai G, Lu A, Xie L, Li H, Zheng M, Sun J. Artificial Intelligence in Decision-Making for Colorectal Cancer Treatment Strategy: An Observational Study of Implementing Watson for Oncology in a 250-Case Cohort. Frontiers in Oncology 2021;10 View
  5. Mao C, Yang X, Zhu C, Xu J, Yu Y, Shen X, Huang Y. Concordance Between Watson for Oncology and Multidisciplinary Teams in Colorectal Cancer: Prognostic Implications and Predicting Concordance. Frontiers in Oncology 2020;10 View
  6. Wilhelm D, Müller-Stich B, Ostler D, Schmitz-Rixen T, Feussner H. Positionspapier „Digitalisierung in der Chirurgie“ – Konsequenzen?. Zentralblatt für Chirurgie - Zeitschrift für Allgemeine, Viszeral-, Thorax- und Gefäßchirurgie 2020;145(06):495 View
  7. Keikes L, Kos M, Verbeek X, Van Vegchel T, Nagtegaal I, Lahaye M, Méndez Romero A, De Bruijn S, Verheul H, Rütten H, Punt C, Tanis P, Van Oijen M. Conversion of a colorectal cancer guideline into clinical decision trees with assessment of validity. International Journal for Quality in Health Care 2021;33(2) View
  8. Solanki S, Pandrowala S, Nayak A, Bhandare M, Ambulkar R, Shrikhande S. Artificial intelligence in perioperative management of major gastrointestinal surgeries. World Journal of Gastroenterology 2021;27(21):2758 View
  9. Liu Y, Huo X, Li Q, Li Y, Shen G, Wang M, Ren D, Zhao F, Liu Z, Zhao J, Liu X. Watson for oncology decision system for treatment consistency study in breast cancer. Clinical and Experimental Medicine 2022;23(5):1649 View
  10. Istasy P, Lee W, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review. Journal of Medical Internet Research 2022;24(11):e39748 View
  11. Tong Y. Efficacy of the Chronic Disease Trajectory Model for Nutritional Support in Patients with Gastric Cancer. Nutrition and Cancer 2023;75(1):296 View
  12. Emani S, Rui A, Rocha H, Rizvi R, Juaçaba S, Jackson G, Bates D. Physicians’ Perceptions of and Satisfaction With Artificial Intelligence in Cancer Treatment: A Clinical Decision Support System Experience and Implications for Low-Middle–Income Countries. JMIR Cancer 2022;8(2):e31461 View
  13. Ural Y, Elter T, Yilmaz Y, Hallek M, Datta R, Kleinert R, Heidenreich A, Pfister D, Ayatollahi H. Validation and implementation of a mobile app decision support system for prostate cancer to improve quality of tumor boards. PLOS Digital Health 2023;2(6):e0000054 View
  14. Han C, Pan Y, Liu C, Yang X, Li J, Wang K, Sun Z, Liu H, Jin G, Fang F, Pan X, Tang T, Chen X, Pang S, Ma L, Wang X, Ren Y, Liu M, Liu F, Jiang M, Zhao J, Lu C, Lu Z, Gao D, Jiang Z, Pei J. Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment. Frontiers in Oncology 2023;13 View
  15. Hu Y, Zheng Z, Tang J, Zhang W. Progress in diagnosis and treatment of gastric cancer from the “Five-For-One” holistic treatment model. Asian Journal of Surgery 2023;46(10):4495 View
  16. Oehring R, Ramasetti N, Ng S, Roller R, Thomas P, Winter A, Maurer M, Moosburner S, Raschzok N, Kamali C, Pratschke J, Benzing C, Krenzien F. Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis. Frontiers in Oncology 2023;13 View
  17. Simon Davis D, Ritchie M, Hammill D, Garrett J, Slater R, Otoo N, Orlov A, Gosling K, Price J, Yip D, Jung K, Syed F, Atmosukarto I, Quah B. Identifying cancer-associated leukocyte profiles using high-resolution flow cytometry screening and machine learning. Frontiers in Immunology 2023;14 View
  18. Thavanesan N, Bodala I, Walters Z, Ramchurn S, Underwood T, Vigneswaran G. Machine learning to predict curative multidisciplinary team treatment decisions in oesophageal cancer. European Journal of Surgical Oncology 2023;49(11):106986 View
  19. Park T, Gu P, Kim C, Kim K, Chung K, Kim T, Jung H, Yoon S, Oh J. Artificial intelligence in urologic oncology: the actual clinical practice results of IBM Watson for Oncology in South Korea. Prostate International 2023;11(4):218 View
  20. Hodroj K, Pellegrin D, Menard C, Bachelot T, Durand T, Toussaint P, Dufresne A, Mery B, Tredan O, Goulvent T, Heudel P. A Digital Solution for an Advanced Breast Tumor Board: Pilot Application Cocreation and Implementation Study. JMIR Cancer 2023;9:e39072 View
  21. Park Y, Chae H. The Fidelity of Artificial Intelligence to Multidisciplinary Tumor Board Recommendations for Patients with Gastric Cancer: A Retrospective Study. Journal of Gastrointestinal Cancer 2024;55(1):365 View
  22. Gairola S, Solanki S, Patkar S, Goel M. Artificial Intelligence in Perioperative Planning and Management of Liver Resection. Indian Journal of Surgical Oncology 2024;15(S2):186 View
  23. Hendriks M, Jager A, Ebben K, van Til J, Siesling S. Clinical decision support systems for multidisciplinary team decision-making in patients with solid cancer: Composition of an implementation model based on a scoping review. Critical Reviews in Oncology/Hematology 2024;195:104267 View
  24. Li S, Li Z, Xue K, Zhou X, Ding C, Shao Y, Zhang S, Ruan T, Zheng M, Sun J. GC-CDSS: Personalized gastric cancer treatment recommendations system based on knowledge graph. International Journal of Medical Informatics 2024;185:105402 View
  25. Shukla A, Chaudhary R, Nayyar N. Role of artificial intelligence in gastrointestinal surgery. WArtificial Intelligence in Cancer 2024;5(2) View

Books/Policy Documents

  1. Vyas S, Bhargava D. Smart Health Systems. View
  2. Singh R, Masih G, Joshi R, Sharma S, Singh A, Medhi B. Biomarkers in Cancer Detection and Monitoring of Therapeutics. View