The Lancet Digital Health · 2023 · 22 citations · 5 references
ComputingComputer ScienceHealth InformaticsArtificial IntelligenceHealth Technology AssessmentMachine LearningArtificial Intelligence PolicyImaging Data GovernanceImaging Data ManagementMedical Image AnalysisMedical ImagingMedical Imaging Information SystemsCross-border Data Sharing AgreementsHealth Ai GovernanceImage Data GovernanceImaging Data IntegrationMachine Learning For HealthcarePublic Health Data ExchangePublic Health Data Sharing
A central question in developing artificial intelligence (AI) for the interpretation of medical images is whether these algorithms will work safely and effectively across diverse patient populations and clinical settings.1 Public datasets are the basis for training and validating AI models, making them essential for the rigorous assessment of performance and reliability that is required by regulatory bodies such as the US Food and Drug Administration.2,3 However, current public datasets seldom have the diversity required to adequately evaluate algorithmic generalisability.
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