Research

Borne 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

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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

Pre-Print

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

In collaboration with researchers at Indiana University

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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

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

In collaboration with researchers at Vanderbilt University

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Artificial intelligence for fetal sex determination

In collaboration with researchers at Brigham and Women’s Hospital

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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

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

In collaboration with researchers at MIT and Stanford

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The Viability of an Artificial Intelligence/Machine Learning Prediction Model to Determine Candidates for Knee Arthroplasty

In collaboration with researchers at Mount Carmel Health System

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

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