Sold by: jCentricity
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OmniMed provides developers and healthcare researchers with direct access to fine-tuned Medical Large Language Models (LLMs). Built on the robust OpenMD framework, our API is specifically optimized for clinical accuracy, medical terminology, and complex healthcare reasoning tasks that general-purpose LLMs often struggle with.
Overview
OmniMed provides developers and healthcare researchers with direct access to fine-tuned Medical Large Language Models (LLMs). Built on the robust OpenMD framework, our API is specifically optimized for clinical accuracy, medical terminology, and complex healthcare reasoning tasks that general-purpose LLMs often struggle with.
With a simple API call to our OmniMed API you can quickly and accurately extract Clinical Named Entities from medical and clinical text.
OmniMed is fully managed, so there are no servers to provision, and no machine learning models to build, train, or deploy. You pay only for what you use, and there are no minimum fees and no upfront commitments.
OmniMed provides the specialized "brain" required for research and educational environments.
OmniMed is provided solely as a research and pedagogical resource. It has not been cleared or approved by the FDA (or other regulatory bodies) for clinical use. Any application of its outputs in a real-world medical context is strictly at the user's own risk.
Highlights
- Fine-Tuned Clinical Accuracy: Designed specifically for healthcare researchers and developers, OmniMed utilizes models optimized for complex medical terminology and clinical reasoning that often exceed the capabilities of general-purpose LLMs.
- Effortless Integration and Scalability: As a fully managed service, OmniMed removes the need for provisioning servers or deploying models, offering a developer-friendly RESTful design that integrates seamlessly with existing workflows in any programming language.
- Specialized Medical Entity Extraction: The API provides high-precision Clinical Named Entity Recognition (NER) by supporting a wide range of specialized models, enabling the rapid processing of biomedical documents and research papers.
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Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
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Dimension | Description | Cost/request |
|---|---|---|
Standard Based Usage | Standard Usage For Analyze Request | $0.05 |
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API-Based Agents & Tools
API-Based Agents and Tools integrate through standard web protocols. Your applications can make API calls to access agent capabilities and receive responses.
Additional details
Usage instructions
API
OmniMed (Clinical NER Detection) API Documentation
Overview
This API provides clinical text analysis and entity extraction capabilities.
OmniMed supports all Medical & Clinical NER models and model names listed at
https://huggingface.co/collections/OpenMed/medical-and-clinical-ner
See Swagger API doc at
http://www.jcentricity.com/omnimed_swagger.json
Include full model name as noted on hugginface.co such as "OpenMed/OpenMed-NER-OncologyDetect-SuperClinical-434M" as model_name parameter in the request
- Base Path: /aws/api
- Host: services.forecastica.com
- Version: 1.0.0
Authentication
This API uses Bearer token authentication.
- Security Definition: Bearer (API Key)
- Header Name: Authorization
- Format: Bearer <API-KEY> (Your API KEY was returned in JSON during registration)
Endpoints
1. Synchronous Analysis
Analyzes clinical text synchronously.
- Endpoint: POST /analyze
- Request Body (AnalyzeRequest):
- text (string): Clinical text to analyze.
- model_name (string): Name of the model to use.
- confidence_threshold (number): Minimum confidence for entity detection.
- group_entities (boolean): Whether to group entities.
- aggregation_strategy (string): Strategy for aggregation.
- Responses:
- 200: Successful analysis (returns AnalyzeResponse)
2. Asynchronous Analysis
Submits clinical text for asynchronous analysis.
- Endpoint: POST /analyze/asynch
- Request Body (AnalyzeRequest): Same as /analyze.
- Responses:
- 202: Job accepted for processing.
3. Retrieve Asynchronous Job Results
Retrieves results of an asynchronous analysis job.
- Endpoint: GET /analyze/jobId/{jobId}
- Parameters:
- jobId (string, path): Unique ID of the job.
- Responses:
- 200: Job completed successfully (returns AnalyzeResponse).
- 202: Job still processing.
- 500: Processing failed.
Definitions
AnalyzeResponse
- data (object):
- text (string)
- entities (array of objects):
- text (string)
- label (string)
- confidence (number)
- start (integer)
- end (integer)
- modelName (string)
- timestamp (string)
- processingTime (number)
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