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Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges.
Giacobbe, Daniele Roberto; Zhang, Yudong; de la Fuente, José.
Affiliation
  • Giacobbe DR; Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.
  • Zhang Y; Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Italy.
  • de la Fuente J; School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK.
Ann Med ; 55(2): 2286336, 2023.
Article in En | MEDLINE | ID: mdl-38010090
AI and ML are revolutionizing human activities in various fields, and infectious diseases are not exempt from their rapid and exponential growth.Despite some notable challenges, explainable AI/ML could provide insights into the decision-making process, making the outcomes of models more transparent.Improved transparency can help to build trust among healthcare professionals, policymakers, and the general public in leveraging AI/ML-based systems to face the growing challenges of infectious diseases in the present century.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Communicable Diseases / COVID-19 Limits: Humans Language: En Journal: Ann Med Journal subject: MEDICINA Year: 2023 Document type: Article Affiliation country: Italy Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Communicable Diseases / COVID-19 Limits: Humans Language: En Journal: Ann Med Journal subject: MEDICINA Year: 2023 Document type: Article Affiliation country: Italy Country of publication: United kingdom