Invisible clinical labor driving the successful integration of AI in healthcare

Mara Ulloa*, Blaine Rothrock, Faraz S. Ahmad, Maia Lee Jacobs

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Artificial Intelligence and Machine Learning (AI/ML) tools are changing the landscape of healthcare decision-making. Vast amounts of data can lead to efficient triage and diagnosis of patients with the assistance of ML methodologies. However, more research has focused on the technological challenges of developing AI, rather than the system integration. As a result, clinical teams' role in developing and deploying these tools has been overlooked. We look to three case studies from our research to describe the often invisible work that clinical teams do in driving the successful integration of clinical AI tools. Namely, clinical teams support data labeling, identifying algorithmic errors and accounting for workflow exceptions, translating algorithmic output to clinical next steps in care, and developing team awareness of how the tool is used once deployed. We call for detailed and extensive documentation strategies (of clinical labor, workflows, and team structures) to ensure this labor is valued and to promote sharing of sociotechnical implementation strategies.

Original languageEnglish (US)
Article number1045704
JournalFrontiers in Computer Science
StatePublished - Dec 1 2022


  • artificial intelligence
  • decision support systems
  • healthcare
  • human-AI collaboration
  • sociotechnical systems

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


Dive into the research topics of 'Invisible clinical labor driving the successful integration of AI in healthcare'. Together they form a unique fingerprint.

Cite this