A Unofficial Python SDK that makes it easy to work with the iMednet REST API from your code or the command line. Manage studies, subjects, records, and more with a "friendly" interface. No need to worry about the low-level details.

🌐 Transform FHIR data into a semantic graph to derive clinical findings, enabling explainable health AI and decision support.

Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries, MCP server and FHIR import.

Alethio Therapeutics Python Toolkit

A collection of useful prompts I've made. Predominant focus for development and clinical trials.

A Nextflow pipeline to generate figures for the Artemis paper

ETL for PRIME-ROSE data to OMOP CDM

“World’s first zero-query clinical data platform – validated on 4,000+ Phase III patients”

BioMCP: Biomedical Model Context Protocol

ClinTrialFinder - AI-powered clinical trial discovery and ranking tool Find the most relevant clinical trials with GPT-4.1-mini filtering, Perplexity AI evidence analysis, and AI-powered ranking. Try via email: ai@mail.clintrialfinder.info - send clinical summary for trial matching. Perfect for researchers and medical professionals.

AI-Powered Deep Research Agent for Sexual Health

Lightweight RTF composer for Python. Specializes in precise formatting of production-quality tables and figures.

Singer tap for ClinicalTrials.gov study records data.

Pack and unpack source projects of any language into portable text files

A small Parser API project that fetches clinical trial data from ClinicalTrials.gov (OAS v2 API), normalizes it, and stores it in MongoDB. Project provides an update mechanism based on lastUpdatePostDateStruct.date and exposes a FastAPI backend.

A self-learning, multi-agent AI for accelerating Alzheimer’s research. From data to insights—fully autonomous and open-source.

META3 - Metformin treatment for diabetes prevention in Africa: META Trial, TASO, MRC/UVRI/LSHTM, NIMR – TZ and Liverpool School of Tropical Medicine (ISRCTN76157257)

TrialMatchAI aims to seamlessly match cancer patients to clinical trials based on their unique genomic and clinical profiles using AI

In this project, you will build Orphan Finder: a compact multi-agent workflow (3 agents) that ranks variants via ClinVar, pulls PMID-backed evidence, finds ClinicalTrials.gov matches, and outputs a clinician-ready, logged, reproducible report. Your solution will help turn hours of scattered manual review into a reproducible, source-attributed brief

LightGBM Based Declarative GPU Accelerated Bionomial Classification Of Clinical Trial Data Equiped With Multiple Vizualizations And Exports. Based On AACT Database.