Evidence map›Paper›PMID 41406939›Full record

ArticleCell reports. Medicine2025

Contrastive learning enhances fairness in pathology artificial intelligence systems.

Shih-Yen Lin, Pei-Chen Tsai, Fang-Yi Su, Chun-Yen Chen, Fuchen Li, Junhan Zhao, Yuk Yeung Ho, Tsung-Lu Michael Lee, Elizabeth Healey, Po-Jen Lin and 19 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
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

29 authors.

Shih-Yen LinDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Pei-Chen TsaiDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan.
Fang-Yi SuDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan.
Chun-Yen ChenDepartment of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan.
Fuchen LiDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Junhan ZhaoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Department of Pediatrics, the University of Chicago, Chicago, IL 60637, USA.
Yuk Yeung HoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.
Tsung-Lu Michael LeeDepartment of Computer Science and Information Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan.
Elizabeth HealeyDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Division of Health Sciences and Technology, Harvard-Massachusetts Institute of Technology, Boston, MA 02139, USA.
Po-Jen LinDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Nuvance Health Danbury Hospital, Danbury, CT 06810, USA.
Ting-Wan KaoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Dmytro VremenkoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Thomas Roetzer-PejrimovskyDivision of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, 1090 Vienna, Austria.
Lynette ShollDepartment of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Pathology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Deborah DillonDepartment of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA.
Nancy U LinDepartment of Medical Oncology, Dana Farber Cancer Institute, Boston, MA 02215, USA.
David MeredithDepartment of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA.
Keith L LigonDepartment of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Pathology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Ying-Chun LoDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA.
Nipon ChaisuriyaDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; Division of Pathology, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand.
David J CookDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA.
Adelheid WoehrerDivision of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria; Department of Neuropathology, Pathology and Molecular Pathology, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Jeffrey MeyerhardtDepartment of Pathology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Shuji OginoDepartment of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute of Science Tokyo, Tokyo 152-8550, Japan.
MacLean P NasrallahDepartment of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA.
Jeffrey A GoldenDepartment of Pathology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Sabina SignorettiDepartment of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Pathology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Jung-Hsien ChiangDepartment of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan. Electronic address: jchiang@mail.ncku.edu.tw.
Kun-Hsing YuDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Data Science Initiative, Harvard University, Cambridge, MA 02134, USA; Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA 02134, USA. Electronic address: kun-hsing_yu@hms.harvard.edu.

Funding

Robust, Generalizable, and Fair Machine Learning Models for BiomedicineR35GM142879 · NIGMS · HARVARD MEDICAL SCHOOL · PI YU, KUN-HSING · 2021 to 2025
$2.4M
Mitigating Hematologic Adverse Events in Patients with Myeloid Malignancies: A Novel Causal Artificial Intelligence ApproachR01HL174679 · NHLBI · HARVARD MEDICAL SCHOOL · PI Kun-Hsing Yu · 2024 to 2026
$2.3M
NHLBI NIH HHS R01 HL174679NIGMS NIH HHS R35 GM142879
6 · The paper itself

Abstract

AI-enhanced pathology evaluation systems hold significant potential to improve cancer diagnosis but frequently exhibit biases against underrepresented populations due to limited diversity in training data. Here, we present the Fairness-aware Artificial Intelligence Review for Pathology (FAIR-Path), a framework that leverages contrastive learning and weakly supervised machine learning to mitigate bias in AI-based pathology evaluation. In a pan-cancer AI fairness analysis spanning 20 cancer types, we identify significant performance disparities in 29.3% of diagnostic tasks across demographic groups defined by self-reported race, gender, and age. FAIR-Path effectively mitigates 88.5% of these disparities, with external validation showing a 91.1% reduction in performance gaps across 15 independent cohorts. We find that variations in somatic mutation prevalence among populations contribute to these performance disparities. FAIR-Path represents a promising step toward addressing fairness challenges in AI-powered pathology diagnoses and provides a robust framework for mitigating bias in medical AI applications.

Indexed as

Artificial IntelligenceMachine LearningNeoplasmsPathologyFemaleHumansMaleAIalgorithmic biasartificial intelligencebias mitigationcancer diagnosiscontrastive learningdeep learningfairnesspathologyweakly supervised learning

Identifiers

PMID41406939
PMCPMC12765949

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.