3 dépôts
Awesome GitHub RepositoriesPrivacy-Preserving Clinical Data AI
Links real-world data to reasoning models for secure, privacy-preserving AI workflows that improve clinical research.
Distinct from Clinical AI Development Kits: Distinct from Clinical AI Development Kits: focuses on privacy-preserving data linkage and reasoning, not general development tools.
Explore 3 awesome GitHub repositories matching scientific & mathematical computing · Privacy-Preserving Clinical Data AI. Refine with filters or upvote what's useful.
Awesome Privacy-Preserving Clinical Data AI GitHub Repositories
Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
- nvidia/isaac-gr00t
NVIDIA/Isaac-GR00T
6,222Voir sur GitHubLinks real-world data to reasoning models for secure, privacy-preserving AI workflows that improve clinical research.
Jupyter Notebook - llsourcell/doctor-dignity
llSourcell/Doctor-Dignity
3,827Voir sur GitHubDoctor-Dignity is a privacy-preserving medical AI framework designed to execute large language models and diagnostic reasoning tasks locally on edge hardware. It provides a local inference engine and retrieval augmented generation implementation that ensures sensitive health data remains offline by removing dependencies on external cloud servers and internet connectivity. The project includes a medical fine-tuning framework for adapting base language models to specialized clinical domains using parameter-efficient methods. To enable execution on resource-constrained and mobile devices, it pro
Implements a framework for secure, privacy-preserving AI workflows that keep sensitive clinical data entirely offline.
Python - mne-tools/mne-python
mne-tools/mne-python
3,243Voir sur GitHubMNE-Python is an open-source Python library for processing, visualizing, and analyzing human neurophysiological data, including MEG, EEG, sEEG, ECoG, and NIRS recordings. It provides a comprehensive framework for loading data from over 30 proprietary file formats into a common hierarchical FIF data structure, and represents all time-series data as NumPy arrays for seamless integration with the scientific Python ecosystem. The library is built around object-oriented data containers that encapsulate raw, epoched, evoked, and source data with built-in preprocessing and visualization methods. The
Processes sEEG, ECoG, and polysomnography data for clinical applications such as sleep staging.
Pythonecogeegelectrocorticography
