Ratings and Reviews 0 Ratings
Ratings and Reviews 0 Ratings
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What is MIM Fertility?
MIM Fertility is dedicated to enhancing the success rates of in vitro fertilization by utilizing artificial intelligence (AI) and data analysis. Our primary goal is to develop AI solutions that empower IVF clinics, allowing patients to achieve their hopes of becoming parents. We aim to elevate the standards of fertility care, transforming the infertility experience while ensuring broader access to fertility options worldwide. Among the cutting-edge tools we offer are AI-powered software platforms like EMBRYOAID and FOLLISCAN. EMBRYOAID supports embryologists and physicians by swiftly evaluating the most viable embryos through image analysis and time-lapse technology, thereby refining the embryo selection process in IVF. In a similar vein, FOLLISCAN functions as a monitoring tool for ovarian follicles, efficiently counting and measuring follicles of various sizes using 2D or 3D cine-loops of the ovaries, which enhances the imaging ultrasonography workflow and improves diagnostic accuracy. By merging innovative technology with fertility treatments, we are confident that we can significantly improve the experiences and outcomes for patients seeking assistance with infertility. Our commitment to these advancements underscores our mission to make fertility solutions more effective and accessible.
What is Amazon Comprehend Medical?
Amazon Comprehend Medical is an NLP service designed to adhere to HIPAA standards, employing machine learning to extract health information from medical documents without necessitating any prior expertise in machine learning from its users. A vast amount of healthcare data is found in unstructured formats, such as physicians' notes, clinical trial reports, and patient histories. Relying on traditional, manual methods for data extraction is not only time-consuming but also prone to errors, as rule-based automation often fails to capture essential contextual details, resulting in incomplete data retrieval. This lack of reliability can significantly undermine the effectiveness of large-scale analytics, which are critical for advancements in the healthcare and life sciences industries, ultimately impeding potential enhancements in patient care and operational effectiveness. By utilizing this sophisticated service, healthcare organizations can gain invaluable insights and improve their decision-making capabilities, ultimately leading to better outcomes for patients. This transformative approach represents a significant leap forward in how health data can be leveraged for greater efficiency and efficacy in medical practices.
Integrations Supported
AWS AI Services
AWS App Mesh
Amazon Comprehend
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
MIM Fertility
Date Founded
2015
Company Location
Poland
Company Website
www.mimfertility.ai/
Company Facts
Organization Name
Amazon
Date Founded
1994
Company Location
United States
Company Website
aws.amazon.com/comprehend/medical/
Categories and Features
Categories and Features
Data Extraction
Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction
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