Healthcare AI for Medical Document Processing and Data ExtractionHealthcare AI for Medical Document Processing and Data Extraction
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Cognate AI — Services We Provide
Medical Document Intelligence Convert unstructured medical documents into structured, usable healthcare data. Technologies: Python, FastAPI, OCR, LLMs, Pydantic, PostgreSQL
Medical OCR & Document Processing Extract text and document structure from scanned medical PDFs, reports, prescriptions, and clinical documents. Technologies: OCR/Vision APIs, Python, document processing pipelines
Clinical Data Extraction Automatically extract:
Diagnoses
Medications & dosages
Symptoms
Lab results
Allergies
Patient information
Clinical dates Technologies: OpenAI, structured LLM outputs, Pydantic, Python
AI-Powered Data Validation Validate extracted information against defined schemas and processing rules before it enters downstream systems. Technologies: Pydantic, Python, rule-based validation, LLM validation
Evidence & Traceability Connect extracted clinical information back to its original document and source location. Technologies: PostgreSQL, evidence tracking, document versioning, hashing
Human-in-the-Loop Review Create workflows where uncertain AI extractions are sent to a reviewer instead of being blindly accepted. Technologies: React, TypeScript, FastAPI, PostgreSQL
FHIR & Healthcare Data Structuring Transform extracted clinical information into structured healthcare formats suitable for integration workflows. Technologies: HL7 FHIR, JSON, Pydantic, Python
Healthcare AI APIs Build APIs that allow healthcare applications to submit documents and receive structured clinical information. Technologies: FastAPI, REST API, Pydantic, Uvicorn
AI Workflow Automation Automate document → OCR → extraction → validation → review → export workflows. Technologies: Python, FastAPI, LangChain/LangGraph, PostgreSQL
Full-Stack Healthcare AI Applications Build complete healthcare AI platforms rather than isolated AI models. Frontend: React, TypeScript, Tailwind CSS Backend: Python, FastAPI, Pydantic Database: PostgreSQL, SQLAlchemy AI: OpenAI, LLM structured extraction Healthcare: OCR, FHIR, clinical schemas Infrastructure: Docker, Git/GitHub, Pytest
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Johnson's avatar
Seeing the human in the loop step catches uncertain extractions before they reach downstream systems, keeping the data pipeline clean and reliable.
Malik sajeel's avatar
Thank you,I really appreciate it. In MDIS, AI performs the initial extraction, and the clinic reviewer reviews and approves the results before they move forward. We believe that human oversight is an important part of building trustworthy AI workflows
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