An open-source Python ecosystem for trustworthy Artificial Intelligence in Healthcare.
Building reliable AI systems for medical research, clinical data, and medical imaging.
Artificial Intelligence in healthcare requires more than high-performing models.
Reliable medical AI begins with reliable medical data, transparent validation, reproducible experimentation, and carefully designed software.
JAINet is an open-source ecosystem created to provide researchers, engineers, clinicians, and healthcare organizations with professional tools for building trustworthy medical AI systems.
Rather than being a single library, JAINet is designed as a long-term ecosystem whose components work together under a unified architecture.
The goal is to simplify the development of reliable healthcare AI while maintaining scientific rigor, software quality, and reproducibility.
The first public release focuses on medical dataset validation.
Currently available validators include:
- Missing value detection
- Duplicate patient detection
- Patient leakage detection
- Label consistency validation
- Class imbalance analysis
- Constant feature detection
- Statistical outlier detection
- Validation pipelines
- Extensible validator architecture
JAINet is being developed incrementally.
Future versions will gradually expand beyond dataset validation to support additional components of medical AI workflows.
Planned directions include:
- Medical image preprocessing utilities
- Medical image analysis tools
- Utilities for building reproducible AI pipelines
- New deep learning architectures designed specifically for healthcare applications
- Experimental medical neural network families developed within the JAINet ecosystem
- Additional tools that improve the reliability, transparency, and reproducibility of medical AI research
The project intentionally follows a modular architecture so that new capabilities can be added without affecting existing workflows.
JAINet is built around several core principles:
- Simple and intuitive public API
- Modular architecture
- Extensible components
- Type-safe design
- Reproducible research
- Clean object-oriented architecture
- Comprehensive documentation
- High test coverage
- Production-quality engineering
from jainet import ValidationPipeline
from jainet.validation import (
MissingValuesValidator,
OutlierValidator,
ConstantColumnsValidator,
)
pipeline = ValidationPipeline(
MissingValuesValidator(),
OutlierValidator(),
ConstantColumnsValidator(),
)
report = pipeline.run(dataframe)pip install jainetJAINet is currently under active development.
jainet/
│
├── core/
├── validation/
├── preprocessing/
├── models/
├── datasets/
├── reports/
├── utils/
└── tests/
The architecture is intentionally modular to support future expansion while preserving a clean and stable public API.
Contributions are welcome.
You can contribute by:
- Reporting bugs
- Improving documentation
- Writing tests
- Implementing new validators
- Optimizing algorithms
- Developing new modules
- Improving code quality
Please read the contribution guidelines before submitting a pull request.
JAINet is under active development.
The current focus is establishing a robust foundation for medical dataset validation.
Future releases will gradually extend the ecosystem toward medical imaging, healthcare-focused deep learning, and additional tools for trustworthy medical AI.
Released under the MIT License.
See the LICENSE file for details.