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ease
features
design
support

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Description

Utilizing our digital twin platform, Twinit.io, BIM Classify serves as a robust foundation for your digital twin projects. This collaborative, cloud-based software includes authoring tool plug-ins designed to assist modelers in accurately and consistently classifying their model content. You can create and manage the necessary classification systems, enabling the creation of equivalencies between different classification systems as required. Additionally, the software offers plug-in recommendations for BIM authoring tools, ensuring that your entire team can uniformly classify elements within the models, thereby enhancing your understanding of model contents. By leveraging BIM, you can derive greater value for various workflows, such as estimating, planning, and creating digital twins. Administrators can easily configure classification systems, mapping rules, and equivalences through the BIM Classify web app. Modelers benefit from the BIM Classify Revit plugin, which streamlines the classification process for their model content. As a result, users of BIM data can access precise information for estimates, quantity takeoffs, asset management, and several other applications. This comprehensive approach not only strengthens collaboration but also boosts overall efficiency in project execution.

Description

Progressing artificial intelligence to remove the need for trial and error in healthcare, our digital twins facilitate swift and assured clinical trials. We focus on areas such as neuroscience, immunology, and metabolic diseases, among others. TwinRCTs expedite full enrollment by requiring fewer participants to provide equivalent statistical power compared to conventional trial methodologies. This approach significantly reduces the time needed for late-stage study enrollment. Additionally, TwinRCTs enhance the ability to detect treatment effects in early-stage studies by bolstering statistical power without necessitating an increase in participant numbers. They enable researchers to make informed decisions based on initial study outcomes and help attract more participants to trials. By utilizing smaller control groups, TwinRCTs also improve participants' odds of receiving the experimental treatment. Our commitment to positioning clinical trials with digital twins for regulatory success is unwavering. Unlearn is at the forefront of transforming the medical field through the innovative application of artificial intelligence, creating and implementing novel generative models that are trained on vast datasets derived from previous patient studies. This evolution in methodology not only streamlines research but also enhances the overall effectiveness of clinical trials.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Revit

Integrations

Revit

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Invicara

Country

United States

Website

invicara.com/platform/products/bim-classify

Vendor Details

Company Name

Unlearn

Country

United States

Website

www.unlearn.ai/

Product Features

BIM

2D Drawing
3D Modeling
Change Management
Collaboration
Conflict Tracking
Design Modeling
Document Management
For Facility Management
Mobile Access
Scheduling

Product Features

Clinical Trial Management

21 CFR Part 11 Compliance
Document Management
Electronic Data Capture
Enrollment Management
HIPAA Compliant
Monitoring
Patient Database
Recruiting Management
Scheduling
Study Planning

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