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Our platform is meticulously crafted with a focus on regulatory compliance, enhancing the landscape of clinical research by employing advanced algorithms to analyze unstructured EMR data for the identification of critical health conditions. We are at the forefront of innovation, developing state-of-the-art technologies that expand access to transformative healthcare solutions through Clinical Trials. Our passionate team is driven by a mission to save lives by improving access to therapeutic advancements still under investigation. We firmly believe that every patient deserves to be informed and empowered about their options to participate in clinical trials that may provide substantial therapeutic advantages. As a progressive healthcare AI research organization, we have developed a sophisticated computational platform that accurately identifies patients who fulfill an extensive range of criteria necessary for participation in clinical trials, ensuring that those best suited are prioritized for these vital studies. Ultimately, our goal is to transform the way patients discover and engage with potentially life-altering treatment options, fostering a new era of healthcare possibilities. Through these efforts, we aspire to bridge the gap between innovative research and patient access, making a tangible difference in the healthcare landscape.
What is Alife?
Alife presents an innovative platform powered by artificial intelligence that transforms clinic workflows, ultimately improving patient outcomes, increasing operational efficiency, and enhancing the overall experience for patients. Their Alife Assist platform is designed to be adaptable, enabling all clinic personnel to make informed, data-based decisions. Seamlessly integrating with existing electronic medical record systems, this AI software converts raw data into actionable insights in real time. It specifically focuses on optimizing the retrieval of mature oocytes during ovarian stimulation cycles. The comprehensive suite of products is meticulously designed to empower each clinic team member effectively. In addition to this, it standardizes the embryo grading process, which contributes to improved quality control and thorough documentation. By facilitating real-time tracking of essential performance metrics, it notably enhances operational efficiency while conserving critical resources. Studies have shown that interpretable machine learning models can achieve comparable results to embryologists when selecting the best embryos for in vitro fertilization (IVF) procedures. Moreover, research indicates that the AI system may optimize the timing of ovulation trigger injections, potentially resulting in superior outcomes for IVF patients. As more clinics embrace this cutting-edge technology, they may uncover new methods to enhance their workflows and elevate the standard of patient care. The integration of such advanced systems could signify a significant shift in how reproductive health services are delivered, paving the way for future innovations in the field.
Integrations Supported
MySQL
SQL
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Dyania Health
Date Founded
2019
Company Location
United States
Company Website
dyaniahealth.com
Company Facts
Organization Name
Alife
Date Founded
2020
Company Location
United States
Company Website
www.alifehealth.com
Categories and Features
Natural Language Processing
Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization
