Research

Born at the MIT Center for Collective Intelligence, Centaur.ai continues to partner with researchers at leading institutions all over the world to advance the field of AI.

featured research

Nonexpert crowds outperform expert individuals in diagnostic accuracy

In collaboration with researchers at Memorial Sloan Kettering

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Research

Evaluating crowdsourcing for ICU EEG annotation

In Collaboration with the NIH and the National Science Foundation

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The use of LLMs to enhance clinical trial educational materials

In collaboration with Dana Farber Cancer Institute and Loyola University

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Can crowdsourced annotations improve AI-based congestion scoring?

In collaboration with the MICCAI Society in agreement with Springer

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

Presented at the American Academy of Neurology annual meeting

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Real-time near infrared AI using scalable non-expert crowdsourcing

In collaboration with Activ Surgical and The OSU Wexner Medical Center

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Leveraging AI to determine fetal sex

Published at the Society of Maternal Fetal Medicine Annual Conference

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Expert-level annotation quality via gamified crowdsourcing

In collaboration with researchers at Brigham and Women’s Hospital

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Boosting the wisdom of the crowd for image annotation

In collaboration with researchers at Indiana University

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On-demand EEG education through competition

In collaboration with researchers at Massachusetts General Hospital

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Gamified crowdsourcing for dataset labeling

In collaboration with researchers at Brigham and Women’s Hospital

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Deep learning for detection and localization

Originally published in the IEEE Journal of Biomedical and Health Informatics

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Comparison of pulmonary congestion severity using AI-assisted scoring versus clinical experts

An analysis from the NHLBI-sponsored BLUSHED-AHF study

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Crowd-sourcing delineations of disease affected skin in patient photographs

Originally published in JMIR Dermatology

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Artificial intelligence for fetal sex determination

Originally published in the American Journal of Obstetrics & Gynecology

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Agreement between experts and an untrained crowd fusing a gamified app

Originally published in JMIR Medical Informatics

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Towards transparency in image datasets with annotations by experts, crowds, and an algorithm

In collaboration with researchers at MIT and Stanford

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The viability of an AI/ML learning prediction model to determine surgical candidates

Originally published in the The Journal of Arthroplasty

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How one block of trials influences the next

In collaboration with researchers at Brigham and Women’s Hospital

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The American Journal of Psychotherapy, Both Old and New

Originally published in the American Journal of Psychotherapy