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A pathologist-AI collaboration framework for enhancing diagnostic accuracies and efficiencies.

Zhi Huang^1,2, Eric Yang¹, Jeanne Shen¹

¹Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.

Nature Biomedical Engineering

|June 19, 2024

Summary

Artificial intelligence (AI) in pathology faces data and transparency challenges. The nuclei.io framework, using active learning and human feedback, improves diagnostic accuracy and efficiency in digital pathology applications.

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Area of Science:

Digital pathology
Artificial intelligence in medicine
Computational pathology

Background:

AI deployment in clinical pathology is hindered by data collection issues and a lack of model transparency and interpretability.
Developing robust AI tools for pathology requires efficient methods for creating diverse datasets and interpretable models.

Purpose of the Study:

To introduce nuclei.io, a digital pathology framework designed to overcome limitations in AI data collection and model interpretability.
To validate the effectiveness of nuclei.io through user studies involving collaboration between AI and pathologists.

Main Methods:

The nuclei.io framework incorporates active learning and human-in-the-loop real-time feedback mechanisms.
Framework validation involved two crossover user studies focusing on plasma cell identification in endometrial biopsies and colorectal cancer metastasis detection in lymph nodes.

Main Results:

The nuclei.io framework demonstrated considerable improvements in diagnostic performance in both validation studies.
The collaborative approach between AI and pathologists using nuclei.io enhanced diagnostic accuracies and operational efficiencies.

Conclusions:

The nuclei.io framework effectively addresses key challenges in AI implementation in digital pathology.
Enhanced collaboration between clinicians and AI systems is crucial for advancing accuracy and efficiency in pathology diagnostics.