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Physicians' Perceptions of and Satisfaction With Artificial Intelligence in Cancer Treatment: A Clinical Decision Support System Experience and Implications for Low-Middle-Income Countries.
Emani, Srinivas; Rui, Angela; Rocha, Hermano Alexandre Lima; Rizvi, Rubina F; Juaçaba, Sergio Ferreira; Jackson, Gretchen Purcell; Bates, David W.
Afiliación
  • Emani S; Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Rui A; Department of Behavioral, Social, and Health Education Sciences, Emory University, Atlanta, GA, United States.
  • Rocha HAL; Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
  • Rizvi RF; Department of Community Health, Federal University of Cearrá, Fortaleza, CE, Brazil.
  • Juaçaba SF; Instituto do Câncer do Ceará, Fortaleza, CE, Brazil.
  • Jackson GP; IBM Watson Health, IBM, Cambridge, MA, United States.
  • Bates DW; Instituto do Câncer do Ceará, Fortaleza, CE, Brazil.
JMIR Cancer ; 8(2): e31461, 2022 Apr 07.
Article en En | MEDLINE | ID: mdl-35389353
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: JMIR Cancer Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: JMIR Cancer Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos