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Description

Citeline serves as a robust platform for clinical development intelligence and analytics, offering life sciences organizations access to real-time research and development data, insights, and tools essential for making informed strategic choices throughout the drug and device development process. By compiling and refining the most comprehensive datasets related to global clinical trials, investigators, trial sites, drug development pipelines, and regulatory stipulations, it aids various functions including protocol design, feasibility assessments, selection of sites and investigators, enrollment predictions, competitive analysis, and tracking compliance with disclosure regulations in different jurisdictions. The platform encompasses several products, such as Pharmaprojects for monitoring drug pipelines, Trialtrove for benchmarking clinical trials, Sitetrove for gathering site and investigator intelligence, Global Patient Insights for ensuring feasibility aligns with actual patient access, and TrialScope Intelligence/Disclose for navigating regulatory disclosure requirements. With its wide range of functionalities, Citeline stands out as a vital resource for organizations aiming to streamline their clinical development processes and enhance their strategic decision-making capabilities. Moreover, the integration of these tools facilitates a more efficient approach to managing the complexities of clinical trials and regulatory compliance in an ever-evolving landscape.

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

No details available.

Integrations

No details available.

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

Citeline

Country

United States

Website

www.citeline.com

Vendor Details

Company Name

Unlearn

Country

United States

Website

www.unlearn.ai/

Product Features

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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