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From Big Data's 5Vs to clinical practice's 5Ws: enhancing data-driven decision making in healthcare.
Bellini, Valentina; Cascella, Marco; Montomoli, Jonathan; Bignami, Elena.
Afiliación
  • Bellini V; Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, Parma, 43126, Italy.
  • Cascella M; Department of Anesthesia and Critical Care, Istituto Nazionale Tumori - IRCCS, Fondazione Pascale, Via Mariano Semmola, 53, Naples, 80131, Italy.
  • Montomoli J; Department of Anesthesia and Intensive Care, Infermi Hospital, AUSL Romagna, Viale Settembrini 2, Rimini, 47923, Italy.
  • Bignami E; Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, Parma, 43126, Italy. elenagiovanna.bignami@unipr.it.
J Clin Monit Comput ; 37(5): 1423-1425, 2023 10.
Article en En | MEDLINE | ID: mdl-37097338
ABSTRACT
The use of AI-based algorithms is rapidly growing in healthcare, but there is still an ongoing debate about how to manage and ensure accountability for their clinical use. While most of the studies focus on demonstrating a good algorithm performance it is important to acknowledge that several additional steps are needed for reaching an effective implementation of AI-based models in daily clinical practice, with implementation being one of the main key factors. We propose a model characterized by five questions that can guide in this process. Additionally, we believe that a hybrid intelligence, human and artificial respectively, is the new clinical paradigm that offer the most benefits for developing clinical decision support systems for bedside use.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Macrodatos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Clin Monit Comput Asunto de la revista: INFORMATICA MEDICA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Macrodatos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Clin Monit Comput Asunto de la revista: INFORMATICA MEDICA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Italia