Year: 2023
Type: article
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.
Source: The Lancet Digital Health
Institutions: Brigham and Women's Hospital, Harvard University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $5,780
OpenAlex ID: W4388713815