Medical LLMJohn Snow Labs
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Neurula ScribeNeurula Technologies
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Related Products
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About
John Snow Labs' Medical LLM is an advanced, domain-specific large language model (LLM) designed to revolutionize the way healthcare organizations harness the power of artificial intelligence. This innovative platform is tailored specifically for the healthcare industry, combining cutting-edge natural language processing (NLP) capabilities with a deep understanding of medical terminology, clinical workflows, and regulatory requirements. The result is a powerful tool that enables healthcare providers, researchers, and administrators to unlock new insights, improve patient outcomes, and drive operational efficiency. At the heart of the Healthcare LLM is its comprehensive training on vast amounts of healthcare data, including clinical notes, research papers, and regulatory documents. This specialized training allows the model to accurately interpret and generate medical text, making it an invaluable asset for tasks such as clinical documentation, automated coding, and medical research.
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About
Neurula Scribe is an ambient AI medical documentation tool delivered as a Chrome extension for clinicians using browser-based electronic medical records. During a consultation, it transcribes speech in real time, distinguishes speakers, and supports English, Arabic, Hindi, Urdu, and Tagalog, including mixed-language conversations. It generates an English SOAP note with ICD-10 coding hints and can autofill the open EMR form for clinician review before approval and signing. The workflow is designed to work with web-based EMRs without an EMR integration project. Neurula Scribe is intended for doctors and healthcare teams in clinics and hospitals, particularly multilingual practices documenting patient visits. The vendor states that audio, transcripts, and notes are held in memory during the session and are not retained on its servers after the session ends.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Not Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Supported
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Audience
Healthcare providers looking for an AI solution to summarize medical texts and extract information
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Audience
Doctors, clinics and hospitals using web-based EMRs, especially multilingual practices in the UAE and GCC
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Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Not Supported
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API
Offers API
Not Supported
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Screenshots and Videos |
Screenshots and VideosNo images available
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Supported
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Pricing
Contact for pricing
Pricing is quoted per clinic based on the number of clinicians. Pilot participation is by request.
Free Version
Not Supported
Free Trial
Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Supported
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Training
Documentation
Not Supported
Webinars
Not Supported
Live Online
Supported
In Person
Not Supported
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Company InformationJohn Snow Labs
United States
www.johnsnowlabs.com/healthcare-llm/
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Company InformationNeurula Technologies
Founded: 2019
United Arab Emirates
neurulatechnologies.com/neurula-scribe.html
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Alternatives |
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Categories |
Categories |
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Integrations
Amazon Web Services (AWS)
Supported
Apache Spark
Supported
Azure Databricks
Supported
Databricks
Supported
Defog
Supported
FLAN-T5
Supported
GPT-3.5
Supported
GPT-4
Supported
Llama 2
Supported
Microsoft Azure
Supported
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Integrations
Amazon Web Services (AWS)
Not Supported
Apache Spark
Not Supported
Azure Databricks
Not Supported
Databricks
Not Supported
Defog
Not Supported
FLAN-T5
Not Supported
GPT-3.5
Not Supported
GPT-4
Not Supported
Llama 2
Not Supported
Microsoft Azure
Not Supported
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