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AI Integration in the Clinical Workflow

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Abstract

Machine learning and artificial intelligence (AI) algorithms hold significant promise for addressing important clinical needs when applied to medical imaging; however, integration of algorithms into a radiology department is challenging. Vended algorithms are integrated into the workflow, successfully, but are typically closed systems and unavailable for site researchers to deploy algorithms. Rather than AI researchers creating one-off solutions, a general, multi-purpose integration system is desired. Here, we present a set of use cases and requirements for a system designed to enable rapid deployment of AI algorithms into the radiologist’s workflow. The system uses standards-compliant digital imaging and communications in medicine structured reporting (DICOM SR) to present AI measurements, results, and findings to the radiologist in a clinical context and enables acceptance or rejection of results. The system also implements a feedback mechanism for post-processing technologists to correct results as directed by the radiologist. We demonstrate integration of a body composition algorithm and an algorithm for determining total kidney volume for patients with polycystic kidney disease.

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Acknowledgements

The authors are indebted to Scott Inglett, David Scheid, and David McGaa from Development Shared Services for the initial implementation of the ROCKET system, and to William Ryan for DICOM SR and fruitful use case discussions. The authors acknowledge Sonia Watson, PhD, and Lucy Bahn, PhD, for their assistance in editing this manuscript.

Funding

This work was funded by internal departmental resources.

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Authors and Affiliations

  1. Mayo Clinic Rochester, 200 First Street SW, Rochester, MN, 55905, USA

    Daniel J. Blezek, Lonny Olson-Williams, Andrew Missert & Pangiotis Korfiatis

Authors
  1. Daniel J. Blezek
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  2. Lonny Olson-Williams
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  3. Andrew Missert
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  4. Pangiotis Korfiatis
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Contributions

Concept and design: D. Blezek, P. Korfiatis, L. Olson-Williams, Drafting the manuscript: D. Blezek, P. Korfiatis, Software development: L. Olson-Williams, Revising the manuscript critical for important intellectual content: P. Korfiatis, L. Olson-Williams, A. Missert, Approval of the manuscript to be published: D. Blezek, P. Korfiatis, L. Olson-Williams, A. Missert.

Corresponding authors

Correspondence to Daniel J. Blezek or Pangiotis Korfiatis.

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The authors declare no competing interests.

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Blezek, D.J., Olson-Williams, L., Missert, A. et al. AI Integration in the Clinical Workflow. J Digit Imaging 34, 1435–1446 (2021). https://doi.org/10.1007/s10278-021-00525-3

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  • DOI: https://doi.org/10.1007/s10278-021-00525-3

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