%0 Journal Article
%@ 2369-1999
%I JMIR Publications
%V 8
%N 2
%P e31461
%T Physicians’ Perceptions of and Satisfaction With Artificial Intelligence in Cancer Treatment: A Clinical Decision Support System Experience and Implications for Low-Middle–Income Countries
%A Emani,Srinivas
%A Rui,Angela
%A Rocha,Hermano Alexandre Lima
%A Rizvi,Rubina F
%A Juaçaba,Sergio Ferreira
%A Jackson,Gretchen Purcell
%A Bates,David W
%+ Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, OBC-3, Boston, MA, 02120, United States, 1 6177327063, semani1@partners.org
%K artificial intelligence
%K cancer
%K low-middle–income countries
%K physicians
%K perceptions
%K Watson for Oncology
%K implementation
%K local context
%D 2022
%7 7.4.2022
%9 Viewpoint
%J JMIR Cancer
%G English
%X As technology continues to improve, health care systems have the opportunity to use a variety of innovative tools for decision-making, including artificial intelligence (AI) applications. However, there has been little research on the feasibility and efficacy of integrating AI systems into real-world clinical practice, especially from the perspectives of clinicians who use such tools. In this paper, we review physicians’ perceptions of and satisfaction with an AI tool, Watson for Oncology, which is used for the treatment of cancer. Watson for Oncology has been implemented in several different settings, including Brazil, China, India, South Korea, and Mexico. By focusing on the implementation of an AI-based clinical decision support system for oncology, we aim to demonstrate how AI can be both beneficial and challenging for cancer management globally and particularly for low-middle–income countries. By doing so, we hope to highlight the need for additional research on user experience and the unique social, cultural, and political barriers to the successful implementation of AI in low-middle–income countries for cancer care.
%M 35389353
%R 10.2196/31461
%U https://cancer.jmir.org/2022/2/e31461
%U https://doi.org/10.2196/31461
%U http://www.ncbi.nlm.nih.gov/pubmed/35389353