Research

Born out of research at the MIT Center for Collective Intelligence, Centaur Labs continues to partner closely with researchers at leading institutions all over the world to advance the field of AI in the medical and life sciences.

featured research

Nonexpert Crowds Outperform Expert Individuals in Diagnostic Accuracy on a Skin Lesion Diagnosis Task

In collaboration with researchers at Memorial Sloan Kettering Cancer Center

The Collective Intelligence Methodology centaur.ai

Want to learn more about our
research-backed methodology?

Download our publications →

Research

The use of large language models to enhance cancer clinical trial educational materials

In collaboration with researchers at Dana Farber Cancer Institute and Loyola University

Research

Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

In collaboration with researchers at Brigham and Women’s Hospital

Research

A randomized controlled educational pilot trial of interictal epileptiform discharge identification for neurology residents

In collaboration with researchers at Beth Israel Deaconess Medical Center and Massachusetts General Hospital

Research

Real-time near infrared artificial intelligence using scalable non-expert crowdsourcing in colorectal surgery

In collaboration with researchers at Activ Surgical and The Ohio State University Wexner Medical Center

Research

Artificial Intelligence to Determine Fetal Sex

In collaboration with researchers at Babyflix and Brigham and Women's Hospital

Research

Expert-Level Annotation Quality Achieved by Gamified Crowdsourcing for B-line Segmentation in Lung Ultrasound

In collaboration with researchers at Brigham and Women’s Hospital

Research

Boosting Wisdom of the Crowd for Medical Image Annotation Using Training Performance and Task Features

In collaboration with researchers at Indiana University

Research

On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform discharges

In collaboration with researchers at Massachusetts General Hospital

Research

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Dataset Labeling

In collaboration with researchers at Brigham and Women’s Hospital

Research

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound

In collaboration with researchers at Brigham and Women’s Hospital

Research

Comparison of pulmonary congestion severity using artificial intellig ence-assisted scoring versus clinical experts: A secondary analysis of BLUSHED-AHF.

In collaboration with researchers at Brigham and Women’s Hospital and Indiana University

Research

Crowd-sourcing delineations of chronic graft-versus-host disease affected skin in patient photographs

In collaboration with researchers at Vanderbilt University

Research

Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study

In collaboration with researchers at Memorial Sloan Kettering Cancer Center

Research

Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm

In collaboration with researchers at MIT and Stanford

Research

How one block of trials influences the next: persistent effects of disease prevalence and feedback on decisions about images of skin lesions in a large online study

In collaboration with researchers at Brigham and Women’s Hospital

Research

The American Journal of Psychotherapy, Both Old and New

In collaboration with researchers at Brigham and Women’s Hospital

Accurate and scalable health data labeling and model evaluation

Copyright © 2025. All rights reserved by Centaur Labs.