The MAIDA initiative: establishing a framework for global medical-imaging data sharing

Abstract

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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