Developing medical imaging AI for emerging infectious diseases

Shih-Cheng Huang (), Akshay S. Chaudhari, Curtis P. Langlotz, Nigam Shah, Serena Yeung and Matthew P. Lungren
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Shih-Cheng Huang: Stanford University
Akshay S. Chaudhari: Stanford University
Curtis P. Langlotz: Stanford University
Nigam Shah: Stanford University
Serena Yeung: Stanford University
Matthew P. Lungren: Stanford University

Nature Communications, 2022, vol. 13, issue 1, 1-6

Abstract: Advances in artificial intelligence (AI) and computer vision hold great promise for assisting medical staff, optimizing healthcare workflow, and improving patient outcomes. The COVID-19 pandemic, which caused unprecedented stress on healthcare systems around the world, presented what seems to be a perfect opportunity for AI to demonstrate its usefulness. However, of the several hundred medical imaging AI models developed for COVID-19, very few were fit for deployment in real-world settings, and some were potentially harmful. This review aims to examine the strengths and weaknesses of prior studies and provide recommendations for different stages of building useful AI models for medical imaging, among them: needfinding, dataset curation, model development and evaluation, and post-deployment considerations. In addition, this review summarizes the lessons learned to inform the scientific community about ways to create useful medical imaging AI in a future pandemic.

Date: 2022
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DOI: 10.1038/s41467-022-34234-4

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