A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies

Zhi Huang1,2, Eric Yang1, Jeanne Shen1, Dita Gratzinger1, Frederick Eyerer1, Brooke Liang1, Jeffrey Nirschl1, David Bingham1, Alex M. Dussaq1, Christian Kunder1, Rebecca Rojansky1, Aubre Gilbert1, Alexandra L. Chang-Graham1, Brooke E. Howitt1, Ying Liu1, Emily E. Ryan1, Troy B. Tenney1, Xiaoming Zhang1, Ann Folkins1, Edward J. Fox1, Kathleen S. Montine1, Thomas J. Montine1,*, James Zou2,*

1 Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
2 Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA

Project Highlights

  • Pathologist-AI collaboration framework.
  • Active learning with human-in-the-loop.
  • Rapidly create a dataset in real-time.
  • Faster and Better diagnosis.

Takeaway message

Nuclei.io enables pathologists to rapidly create diverse datasets and models for multiple clinically critical applications. To validate the effectiveness of this framework, we perform two cross-over user studies using a pathologist–AI collaborative approach. In both studies, the use of AI yielded considerable improvements in sensitivity, certainty, and efficiency in pathological diagnosis. Our findings validate the proposed human-in-the-loop framework, and suggest that pathologist–AI collaboration holds the potential to revolutionize digital pathology, ultimately leading to improved quality, efficiency, and cost.

Nuclei.io software

The repository of nuclei.io software

Access the repo

Nuclei data

The dataset (with RGB image patch and label) that annotated by pathologists.

CRC lymph node metastasis nuclei dataset
Plasma cell nuclei dataset