Developing medical imaging AI for emerging infectious diseases

doi:10.1038/s41467-022-34234-4

. 2022 Nov 18;13(1):7060.

doi: 10.1038/s41467-022-34234-4.

Developing medical imaging AI for emerging infectious diseases

Shih-Cheng Huang^{1

2}, Akshay S Chaudhari^{3

4

5}, Curtis P Langlotz^{3

4

5}, Nigam Shah³, Serena Yeung^#^{3

4

6

7

8}, Matthew P Lungren^#^{3

4

5}

Affiliations

¹ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. mschuang@stanford.edu.
² Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA. mschuang@stanford.edu.
³ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
⁴ Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.
⁵ Department of Radiology, Stanford University, Stanford, CA, USA.
⁶ Department of Computer Science, Stanford University, Stanford, CA, USA.
⁷ Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
⁸ Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA.

^# Contributed equally.

PMID: 36400764
PMCID: PMC9672573
DOI: 10.1038/s41467-022-34234-4

Developing medical imaging AI for emerging infectious diseases

Shih-Cheng Huang et al. Nat Commun. 2022.

. 2022 Nov 18;13(1):7060.

doi: 10.1038/s41467-022-34234-4.

Authors

Shih-Cheng Huang^{1

2}, Akshay S Chaudhari^{3

4

5}, Curtis P Langlotz^{3

4

5}, Nigam Shah³, Serena Yeung^#^{3

4

6

7

8}, Matthew P Lungren^#^{3

4

5}

Affiliations

¹ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. mschuang@stanford.edu.
² Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA. mschuang@stanford.edu.
³ Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
⁴ Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.
⁵ Department of Radiology, Stanford University, Stanford, CA, USA.
⁶ Department of Computer Science, Stanford University, Stanford, CA, USA.
⁷ Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
⁸ Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, USA.

^# Contributed equally.

PMID: 36400764
PMCID: PMC9672573
DOI: 10.1038/s41467-022-34234-4

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

**Fig. 1. The four stages of building useful medical imaging AI models for emerging infectious diseases.**
We provide recommendations and guidelines for the four stages of medical imaging AI development process in each section of this manuscript, namely: needfinding, dataset curation, model development and subsequent evaluation, and post-deployment considerations. Each stage of the development process should be considered when building medical imaging AI for emerging infectious diseases.

See this image and copyright information in PMC

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References

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1. Li MD, et al. Radiology implementation considerations for artificial intelligence (AI) applied to COVID-19, from the AJR special series on AI applications. AJR Am. J. Roentgenol. 2022;219:15–23. doi: 10.2214/AJR.21.26717. - DOI - PubMed
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[1] Data science and AI in the age of COVID-19—report. The Alan Turing Institutehttps://www.turing.ac.uk/research/publications/data-science-and-ai-age-c... (2021).

[2] Data science and AI in the age of COVID-19—report. The Alan Turing Institutehttps://www.turing.ac.uk/research/publications/data-science-and-ai-age-c... (2021).

[3] Roberts M, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 2021;3:199–217. doi: 10.1038/s42256-021-00307-0. - DOI

[4] Roberts M, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 2021;3:199–217. doi: 10.1038/s42256-021-00307-0. - DOI

[5] Li MD, et al. Radiology implementation considerations for artificial intelligence (AI) applied to COVID-19, from the AJR special series on AI applications. AJR Am. J. Roentgenol. 2022;219:15–23. doi: 10.2214/AJR.21.26717. - DOI - PubMed

[6] Li MD, et al. Radiology implementation considerations for artificial intelligence (AI) applied to COVID-19, from the AJR special series on AI applications. AJR Am. J. Roentgenol. 2022;219:15–23. doi: 10.2214/AJR.21.26717. - DOI - PubMed

[7] Wynants L, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi: 10.1136/bmj.m1328. - DOI - PMC - PubMed

[8] Wynants L, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi: 10.1136/bmj.m1328. - DOI - PMC - PubMed

[9] Born J, et al. On the role of artificial intelligence in medical imaging of COVID-19. Patterns. 2021;2:100269. doi: 10.1016/j.patter.2021.100269. - DOI - PMC - PubMed

[10] Born J, et al. On the role of artificial intelligence in medical imaging of COVID-19. Patterns. 2021;2:100269. doi: 10.1016/j.patter.2021.100269. - DOI - PMC - PubMed

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Developing medical imaging AI for emerging infectious diseases

Affiliations

Developing medical imaging AI for emerging infectious diseases

Authors

Affiliations

Abstract

Conflict of interest statement

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