Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging

arXiv:2308.09730v2 Announce Type: replace-cross
Abstract: The credibility of AI models in medical imaging is often challenged by reproducibility issues and obscured clinical insights, a reality highlighted during the COVID-19 pandemic by many reports of near-perfect artificial intelligence (AI) models that all failed to generalize. To address these concerns, we propose a virtual imaging trial framework, employing a diverse collection of medical images that are both clinical and simulated. In this study, COVID-19 serves as a case example to unveil the …

abstract ai models ai systems artificial artificial intelligence arxiv clinical covid covid-19 covid-19 pandemic cs.lg eess.iv imaging insights intelligence medical medical imaging near pandemic reality reliability reports reproducibility systems transparency type virtual