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Ratings and Reviews 0 Ratings
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What is Binah.ai?
Binah.ai is dedicated to making health data more accessible to enhance decision-making while lowering costs. Their cutting-edge health data platform operates solely on software and utilizes AI alongside video technology, allowing various sectors in need of real-time health insights to seamlessly collect a wide range of health metrics from users in an uncomplicated, digital manner. This extensive dataset encompasses essential indicators such as heart rate, heart rate variability (including HRV SDNN and RRI raw data), oxygen levels, respiration rate, and measures of sympathetic and parasympathetic activity, along with the pulse-respiration quotient (PRQ), with blood pressure data to be added shortly. By removing the need for wearables or specialized equipment, Binah.ai’s solution is presented as a software development kit (SDK), which is compatible with a variety of devices and operating systems. This compatibility allows for health and wellness monitoring to be easily conducted on personal electronic devices, such as smartphones, tablets, or laptops that users already possess, fostering a more user-friendly method of health tracking. Moreover, this innovative approach simplifies the data collection process and empowers individuals to take control of their health with minimal obstacles, ultimately leading to a more health-conscious society.
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
Binah.ai
Date Founded
2016
Company Location
Israel
Company Website
www.binah.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