🏥 MedKit: A Unified Platform for Medical Data APIs

MedKit is a high-performance, unified SDK that transforms fragmented medical APIs into a single, programmable platform. It provides a clean interface for OpenFDA, PubMed, and ClinicalTrials.gov, augmented with a clinical intelligence layer and relationship mapping.

Important

v2.0.0 Release: This version introduces a fully asynchronous architecture, robust ClinicalTrials.gov v2 integration with automatic curl fallback, and enhanced clinical synthesis precision.

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✨ Async Example (v2.0.0)

import asyncio
from medkit import AsyncMedKit

async def main():
    async with AsyncMedKit() as med:
        # Unified search across all providers in parallel
        results = await med.search("pembrolizumab")
        
        print(f"Drugs found: {len(results.drugs)}")
        print(f"Clinical Trials: {len(results.trials)}")
        
        # Get a synthesized conclusion
        conclusion = await med.ask("What is the clinical status of Pembrolizumab for NSCLC?")
        print(f"Summary: {conclusion.summary}")
        print(f"Confidence: {conclusion.confidence_score}")

asyncio.run(main())

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🤔 Why MedKit?

Feature	Without MedKit	With MedKit
Integrations	3 separate APIs / SDKs	Unified Sync/Async Client
Resilience	403 blocks from gov APIs	Auto-Fallback (Curl/v2 API)
Synthesis	Alphabetical/Noisy lists	Frequency-Ranked Intervals
Logic	Manual data correlation	Native knowledge graphs
Speed	Sequential network calls	Parallel Async Orchestration

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🏗️ Architecture

MedKit abstracts complexity through a high-performance orchestration layer:

      Developer / User
             │
             ▼
    ┌───────────────────┐
    │  MedKit / Async   │ (Unified Interface)
    └─────────┬─────────┘
              │
    ┌─────────┴─────────────────────┐
    │       Intelligence Layer      │
    │  ├─ Ask Engine (Extraction)   │
    │  ├─ Graph Engine (Context)    │
    │  ├─ Interaction Engine        │
    │  └─ Synthesis Engine (Ranked) │
    └─────────┬─────────────────────┘
              │
    ┌─────────┴─────────────────────┐
    │       Providers Layer         │
    │  ├─ OpenFDA     (Drug Label)  │
    │  ├─ PubMed      (Research)    │
    │  └─ ClinTrials  (v2 + Fallback)│
    └───────────────────────────────┘

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🚀 Core Platform Features

• Robust Connectivity (NEW): Automatic curl fallback for ClinicalTrials.gov bypasses TLS fingerprinting blocks, ensuring 100% data availability.
• Async-First Orchestration: Parallel health checks and search execution eliminate latency bottlenecks and perceived "hangs."
• Precision Evidence Synthesis: Automated clinical verdicts with frequency-ranked interventions and filtered therapeutic agents (Drugs/Biologicals).
• High-Performance CLI: Interactive, list-based visualization for trials and research papers, optimized for all terminal sizes.
• Natural Language Extraction: Robust regex-based term extraction handles typos and complex query structures (e.g., "clinial status of...").
• Unified Caching: Enhanced Disk and Memory caching (with Pydantic support) for high-performance repeat queries.

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📦 Installation

pip install medkit-sdk

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🖥️ CLI Power Tools

Clinical Ask (Synthesized)

$ medkit ask "pembrolizumab for lung cancer"

 Clinical Conclusion
Summary: Highly-validated therapeutic landscape with multi-modal evidence.
Confidence: ████████████████████ 1.00
Top Interventions: Pembrolizumab, Bevacizumab, Carboplatin, Cisplatin

Trials Search

$ medkit trials "melanoma" --limit 5

Clinical Trials for 'melanoma'
- NCT01234567: RECRUITING - Study of Pembrolizumab in Advanced Melanoma
- NCT07654321: COMPLETED - Comparison of B-Raf Inhibitors

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🗺️ Roadmap

• v1.0.0: Foundation medical mesh and provider integration.
• v2.0.0: Async architecture, v2 API support, and synthesis precision.
• v2.1.0: Advanced pharmacogenomics (SNP) provider integration.
• v3.0.0: Local GraphQL medical mesh endpoint.

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📄 License

MIT License - see LICENSE for details.