Generative AI mitigates representation bias and improves model fairness through synthetic health data

• Raffaele Marchesi ,

  Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

  * E-mail: rmarchesi@fbk.eu (RM); v.osmani@qmul.ac.uk (VO)

  Affiliations Data Science for Health (DSH), Fondazione Bruno Kessler, Trento, Italy, Department of Mathematics, University of Pavia, Pavia, Italy

  https://orcid.org/0009-0006-6595-5344

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• Nicolo Micheletti,

  Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft

  Affiliations Data Science for Health (DSH), Fondazione Bruno Kessler, Trento, Italy, Department of Computer Science, University of Manchester, Manchester, United Kingdom

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• Nicholas I-Hsien Kuo,

  Roles Writing – original draft

  Affiliation Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia

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• Sebastiano Barbieri,

  Roles Writing – original draft

  Affiliations Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia, Queensland Digital Health Centre, University of Queensland, Brisbane, Queensland, Australia

  https://orcid.org/0000-0002-5919-372X

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• Giuseppe Jurman,

  Roles Project administration, Supervision, Writing – original draft

  Affiliations Data Science for Health (DSH), Fondazione Bruno Kessler, Trento, Italy, Department of Biomedical Sciences, Humanitas University, Milan, Italy

  https://orcid.org/0000-0002-2705-5728

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• Venet Osmani

  Roles Conceptualization, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

  * E-mail: rmarchesi@fbk.eu (RM); v.osmani@qmul.ac.uk (VO)

  Affiliation Digital Environment Research Institute (DERI), Queen Mary University of London, London, United Kingdom

  https://orcid.org/0000-0001-7306-2972

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