ArticleNature medicine2024
Demographic bias in misdiagnosis by computational pathology models.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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.
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.
Who cites it
68 citing papers in PubMed, 83 citations in OpenAlex.
- Ethical guidelines for deploying artificial intelligence applications in the pathology field: Lessons learned from a prospective framework in a large tertiary care academic medical center.Journal of pathology informatics · 2026Article
- MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis.IEEE transactions on medical imaging · 2026Article
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Artificial Intelligence and Digital Pathology: Technological Transformation and Strategic Impact in Clinical Research and Medical Affairs.Life (Basel, Switzerland) · 2026Review
- Development of a benchmarking dataset for symptom detection using large language models.JAMIA open · 2026Article
- Learning neuroimaging models from health system-scale data.Nature biomedical engineering · 2026Article
- Large Language Models for Optimizing Patient Recruitment Decisions in Voice Data Generation Projects.Laryngoscope investigative otolaryngology · 2026Article
- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 2026Article
- Review
- Towards robust foundation models for digital pathology.Nature communications · 2026Article
- From Biomarker Discovery to Targeted Clinical Application: Addressing Translational Gaps in Early Cancer Detection.Biomedicines · 2026Article
- Agentic AI in global health equity for high altitude populations.NPJ digital medicine · 2026Review
- Educational Frameworks for Diagnostic Decision-Making in AI-Enhanced Head and Neck Pathology.Head and neck pathology · 2026Review
- Clinical Validation of AI-Assisted Evaluation of Indeterminate Biliary Strictures in Digital-Single Operator Cholangioscopy: A Transcontinental Multicentric Study.Clinical and translational gastroenterology · 2026Article
- Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.Nature cancer · 2026Article
- Bridging the interpretability gap for medical artificial intelligence models using class-association manifold learning.Nature biomedical engineering · 2026Article
- Deep learning-based H&E-derived risk scores in colorectal cancer: associations with tumour morphology, biology, and predicted drug response.The Journal of pathology · 2026Article
- Tracing the Pen: Electronic Health Records Amid the Rise of Generative AI.NPJ digital medicine · 2026Review
- CLEAR-IT, a framework for contrastive learning to capture the immune composition of tumor microenvironments.Communications biology · 2026Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 1 country.
Funding
Abstract
Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas. For example, when using common modeling approaches, we observed performance gaps (in area under the receiver operating characteristic curve) between white and Black patients of 3.0% for breast cancer subtyping, 10.9% for lung cancer subtyping and 16.0% for IDH1 mutation prediction in gliomas. We found that richer feature representations obtained from self-supervised vision foundation models reduce performance variations between groups. These representations provide improvements upon weaker models even when those weaker models are combined with state-of-the-art bias mitigation strategies and modeling choices. Nevertheless, self-supervised vision foundation models do not fully eliminate these discrepancies, highlighting the continuing need for bias mitigation efforts in computational pathology. Finally, we demonstrate that our results extend to other demographic factors beyond patient race. Given these findings, we encourage regulatory and policy agencies to integrate demographic-stratified evaluation into their assessment guidelines.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.