Evidence map›Paper›PMID 38898173›Full record

ArticleNature biomedical engineering2025

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

Zhi Huang, Eric Yang, Jeanne Shen, Dita Gratzinger, Frederick Eyerer, Brooke Liang, Jeffrey Nirschl, David Bingham, Alex M Dussaq, Christian Kunder and 13 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Comparative evaluation of large language models and clinicians in real-world glaucoma clinical reasoning.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Zhi HuangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-6982-8285
Eric YangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Jeanne ShenDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Dita GratzingerDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-9182-8123
Frederick EyererDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Brooke LiangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-8823-2804
Jeffrey NirschlDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-6857-341X
David BinghamDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Alex M DussaqDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Christian KunderDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-1514-7550
Rebecca RojanskyDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Aubre GilbertDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Alexandra L Chang-GrahamDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Brooke E HowittDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Ying LiuDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Emily E RyanDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Troy B TenneyDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Xiaoming ZhangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Ann FolkinsDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Edward J FoxDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Kathleen S MontineDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
Thomas J MontineDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA. tmontine@stanford.edu.ORCID http://orcid.org/0000-0002-1346-2728
James ZouDepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA. jamesz@stanford.edu.ORCID http://orcid.org/0000-0001-8880-4764

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In pathology, the deployment of artificial intelligence (AI) in clinical settings is constrained by limitations in data collection and in model transparency and interpretability. Here we describe a digital pathology framework, nuclei.io, that incorporates active learning and human-in-the-loop real-time feedback for the rapid creation of diverse datasets and models. We validate the effectiveness of the framework via two crossover user studies that leveraged collaboration between the AI and the pathologist, including the identification of plasma cells in endometrial biopsies and the detection of colorectal cancer metastasis in lymph nodes. In both studies, nuclei.io yielded considerable diagnostic performance improvements. Collaboration between clinicians and AI will aid digital pathology by enhancing accuracies and efficiencies.

Indexed as

Artificial IntelligencePathologistsBiopsyColorectal NeoplasmsFemaleHumansLymphatic MetastasisLymph Nodes

Identifiers

PMID38898173

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.