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AI is a lot of work

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By Josh Dzieza an investigations editor covering tech

Gamifying medical data labeling to advance AI
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MIT alumnus’ platform taps the wisdom of crowds to label medical data for AI companies

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

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Dataset Labeling

In collaboration with researchers at Brigham and Women’s Hospital

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

In collaboration with researchers at Brigham and Women’s Hospital

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

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

In collaboration with researchers at Vanderbilt University

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

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

In collaboration with researchers at MIT and Stanford

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

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

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

Artificial intelligence for fetal sex determination

In collaboration with researchers at Brigham and Women’s Hospital

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

In collaboration with researchers at Brigham and Women’s Hospital

Featured in

DiagnosUs App Uses Gamification To Fill Gaps in Medical Education

Gamifying medical data labeling to advance AI

AI Is a Lot of Work

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PharmStars Announces Fall 2022 Accelerator Graduates: 11 Digital Health Startups Complete PharmStars’ Pharma-Focused Accelerator Program

Ensuring Clinical AI Safety at Scale: deepc, Centaur Labs, and Segmed form Radiology AI Safety Initiative

Ensuring Clinical AI Safety at Scale: deepc, Centaur Labs, and Segmed form Radiology AI Safety Initiative

Centaur Labs and Dandelion Health partner to enable AI developers to access high quality clinical data at scale

Consensus partners with Centaur Labs to generate high-quality and scalable scientific data labels

Boston Business Journal announces 40 Under 40 honorees for 2022

Completely new healthcare markets and what to build for them

Centaur Labs partners with Mayo Clinic spin out Lucem Health to accelerate medical AI development

Centaur Labs named Top 100 Early-Stage Company to Work for in 2022

The shifting digital health investment landscape in 2022

2021 year-end digital health funding: Seismic shifts beneath the surface

The Digital Health 150: The Top Digital Health Companies Of 2021

Termsheet

Centaur Labs Raises $15 Million, Led by Matrix Partners, to Label the World’s Medical Data, Accelerate AI Development

Centaur Labs Raises $15 Million, Led by Matrix Partners, to Label the World’s Medical Data, Accelerate AI Development

Health care AI startup raises $15M to label medical data

Centaur Labs Gets $15 Million To Improve Data For Healthcare AI

Harnessing the power of a global network of medical experts

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