Products Overview
The Next Generation Of Medical AI
Leverage data like never before and transform your potential into performance.
The emtelligent® medical AI product suite can be deployed to meet the clinical data needs of any healthcare business.
Document Manager
Identify, categorize, and collate paper forms and complex medical documents, digitizing them and making them available for immediate use.
- Document Splitting and Indexing
- Information Extraction
- Optical Character Recognition (OCR)
- And more!
Medical Language Engine
Powerful medical language engine extracts medical terms and concepts at scale, processing unstructured clinical records in seconds. Available custom medical language models provide enhanced accuracy.
- Entity Linking
- Polarity & Uncertainty Detection
- Experiencer Detection
- And more!
Clinical Workflow
AI-powered workflow accelerators designed to automate complex tasks and empower business users to access insights through a conversational interface.
- AI-Enhanced Search
- Document Tree Overview
- Annotation and Criteria Templates
- And more!
The safe, ethical AI that healthcare demands
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Delivered onsite, in a private cloud, or securely hosted
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Continuously updated with the latest in AI methodology
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Carefully trained and rigorously refined
Our medical AI engine is a product of years of development and enhancement, built on a training set of billions of clinical data points, resulting in unparalleled accuracy.
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More than 2 million clinical records, hand-annotated by medical experts
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Enterprise-grade and feature-rich
Flexible deployment options
Our products can be deployed within our environment, within the customer environment, or in a private cloud.
Low lift implementations
Our solutions are easily integrated into a customer’s technology stack and clinical workflow. Our scalable and efficient implementations also ensure you get the lowest computing and runtime costs.
Expert support
Our team will ensure your deployment is successfully delivering the insights and impact you desire.
Data security & privacy
You are in control of your data – client source documents are not utilized in the solutions training data set, ensuring that this data is inaccessible to others. Your inputs and results (outputs):
- Are NOT available to other customers.
- Are NOT used to train or improve emtelligent unless you have opted into optional data sharing.
Robust developer resources
API & SDK documentation and quickstart guides and examples are provided to simplify and accelerate your emtelliPro integration.
Fully auditable output
Connect any data element back to its point of origin in its source document, providing traceability and confidence.
How we stack up
The rigors of healthcare demand a higher level of accuracy than other AI applications. See how emtelligent meets these rigorous standards while others fall short.
Accuracy Comparison
Example statement
“She has a negative Tinel's but a positive Phalen's so I have put her wrist in a splint, and I am waiting on the report from her recent EMG.”
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Amazon |
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Microsoft |
John Snow Labs |
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She |
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negative Tinel's |
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positive Phalen's |
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her |
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wrist |
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splint |
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report |
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recent |
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EMG |
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100% |
10% |
20% |
20% |
40% |
emtelligent vs. Generative AI
Unaligned Generative AI is not accurate enough for medical purposes. It is prone to hallucinations (making up answers) and requires extensive prompt engineering and training. The current architecture of these solutions is also not scalable due to their inability to process, store, and rapidly search structured data extracted from millions of documents.
emtelligent vs. General Purpose NLP
General purpose NLP solutions fail when applied to medical text due to the uniqueness of medical language. Everything we do is linked to medical ontologies and purpose-built for healthcare. Our solution picks up much more than even the best general purpose NLP engine – uncovering the rich information contained in patient experiences, family history, gene names, medication dosages and relationships, and so much more.
emtelligent vs. Existing Medical AI
Other solutions are often narrowly focused and not extensible to additional use cases. These solutions are also generally rules-based, static solutions – not rooted in the most recent advancements in NLP and deep learning, requiring a large and ongoing investment in medical and data science talent.