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Methodology for integrating artificial intelligence in healthcare systems: learning from COVID-19 to prepare for Disease X.
Radanliev, Petar; De Roure, David; Maple, Carsten; Ani, Uchenna.
Afiliação
  • Radanliev P; Department of Engineering Sciences, Oxford e-Research Centre, University of Oxford, Oxford, UK.
  • De Roure D; Department of Engineering Sciences, Oxford e-Research Centre, University of Oxford, Oxford, UK.
  • Maple C; WMG Cyber Security Centre, University of Warwick, Coventry, UK.
  • Ani U; STEaPP, Faculty of Engineering Science, University College London, London, UK.
AI Ethics ; 2(4): 623-630, 2022.
Article em En | MEDLINE | ID: mdl-34790960
Artificial intelligence and edge devices have been used at an increased rate in managing the COVID-19 pandemic. In this article we review the lessons learned from COVID-19 to postulate possible solutions for a Disease X event. The overall purpose of the study and the research problems investigated is the integration of artificial intelligence function in digital healthcare systems. The basic design of the study includes a systematic state-of-the-art review, followed by an evaluation of different approaches to managing global pandemics. The study design then engages with constructing a new methodology for integrating algorithms in healthcare systems, followed by analysis of the new methodology and a discussion. Action research is applied to review existing state of the art, and a qualitative case study method is used to analyse the knowledge acquired from the COVID-19 pandemic. Major trends found as a result of the study derive from the synthesis of COVID-19 knowledge, presenting new insights in the form of a conceptual methodology-that includes six phases for managing a future Disease X event, resulting with a summary map of various problems, solutions and expected results from integrating functional AI in healthcare systems.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Revista: AI Ethics Ano de publicação: 2022 Tipo de documento: Article País de publicação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Revista: AI Ethics Ano de publicação: 2022 Tipo de documento: Article País de publicação: Suíça