CollectiveGood

About CollectiveGood

CollectiveGood operates a platform that utilizes a network of clinical experts to provide consensus assessments of medical AI performance through continuous testing and human evaluation. This approach addresses the need for reliable validation of AI models in healthcare, ensuring their effectiveness and safety in real-world applications.

<problem> The healthcare industry needs reliable methods for validating the performance and safety of medical AI models before deployment. Traditional validation processes often lack the scale and real-world clinical expertise required to ensure AI effectiveness across diverse patient populations and clinical scenarios. </problem> <solution> CollectiveGood offers a platform that leverages a network of verified clinical experts to provide continuous, consensus-based assessments of medical AI performance. The platform uses a review dataset comprised of AI-sample cases and clinician evaluation cases to assess both the AI and the individual crowd members. Cases are directed to clinicians with matching expertise profiles and verified performance, and an aggregation engine determines the crowd consensus on individual cases. The platform consolidates AI case-level review results to provide overall assessment metrics, rationales, and recommendations. </solution> <features> - Access to a network of hundreds of verified clinical experts for AI model evaluation - Continuous testing and validation of AI models through incentivized participation - Scalable human evaluation of AI outputs with short input times - AI-sample cases and clinician evaluation cases to assess both the AI and the individual crowd members - Expert matching to direct cases to clinicians with matching expertise profiles and verified performance - Consensus optimization using an aggregation engine to determine the crowd consensus on individual cases - Consolidation of AI case-level review results to overall assessment metrics, rationales, and recommendations </features> <target_audience> The primary target audience includes healthcare organizations, AI developers, and clinical researchers who require robust validation of medical AI models. </target_audience>

What does CollectiveGood do?

CollectiveGood operates a platform that utilizes a network of clinical experts to provide consensus assessments of medical AI performance through continuous testing and human evaluation. This approach addresses the need for reliable validation of AI models in healthcare, ensuring their effectiveness and safety in real-world applications.

Where is CollectiveGood located?

CollectiveGood is based in Hong Kong.

When was CollectiveGood founded?

CollectiveGood was founded in 2022.

Location
Hong Kong
Founded
2022
0
Looking for specific startups?
Try our free semantic startup search

CollectiveGood

AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

CollectiveGood operates a platform that utilizes a network of clinical experts to provide consensus assessments of medical AI performance through continuous testing and human evaluation. This approach addresses the need for reliable validation of AI models in healthcare, ensuring their effectiveness and safety in real-world applications.

collectivegood.ioLinkedIn Profile
Founded 2022 – Hong Kong

Funding

No funding information available. Click "Fetch funding" to run a targeted funding scan.

Company Description

Problem

The healthcare industry needs reliable methods for validating the performance and safety of medical AI models before deployment. Traditional validation processes often lack the scale and real-world clinical expertise required to ensure AI effectiveness across diverse patient populations and clinical scenarios.

Solution

CollectiveGood offers a platform that leverages a network of verified clinical experts to provide continuous, consensus-based assessments of medical AI performance. The platform uses a review dataset comprised of AI-sample cases and clinician evaluation cases to assess both the AI and the individual crowd members. Cases are directed to clinicians with matching expertise profiles and verified performance, and an aggregation engine determines the crowd consensus on individual cases. The platform consolidates AI case-level review results to provide overall assessment metrics, rationales, and recommendations.

Features

Access to a network of hundreds of verified clinical experts for AI model evaluation

Continuous testing and validation of AI models through incentivized participation

Scalable human evaluation of AI outputs with short input times

AI-sample cases and clinician evaluation cases to assess both the AI and the individual crowd members

Expert matching to direct cases to clinicians with matching expertise profiles and verified performance

Consensus optimization using an aggregation engine to determine the crowd consensus on individual cases

Consolidation of AI case-level review results to overall assessment metrics, rationales, and recommendations

Target Audience

The primary target audience includes healthcare organizations, AI developers, and clinical researchers who require robust validation of medical AI models.