KARMA

KARMA-OpenMedEvalKit is an open-source framework for assessing AI models in medical applications. It supports multiple healthcare-focused datasets and is designed for researchers and developers. The toolkit is extensible, allowing integration of custom models and metrics.

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What is KARMA?

KARMA is an evaluation framework for medical AI systems, providing a toolkit to assess models using various datasets. It is built for researchers and healthcare organizations to ensure reliable performance testing. The system supports multi-modal evaluation and is model-agnostic. **Use cases**: Evaluating medical AI model performance, Testing models on Indian healthcare datasets, Benchmarking text, image, and audio models, Integrating custom evaluation metrics, Running efficient batch evaluations **Key features**: Extensible toolkit for custom models and datasets; Supports text, image, and audio evaluation; Smart caching for faster re-evaluations; Registry-based architecture for auto-discovery; Rich CLI with progress bars and outputs; Model-agnostic with unified interface; Integrated with HuggingFace and local evaluation **Who it's for**: Researchers, developers, and healthcare organizations that need to evaluate medical AI systems reliably. It is especially useful for those working in or with the Indian healthcare environment.

KARMA is an evaluation framework for medical AI systems, providing a toolkit to assess models using various datasets. It is built for researchers and healthcare organizations to ensure reliable performance testing. The system supports multi-modal evaluation and is model-agnostic.

Key features

  • medical AI evaluation
  • multi-modal assessment
  • custom datasets
  • smart caching
  • model agnostic
  • HuggingFace integration
  • CLI toolkit
  • Extensible toolkit for custom models and datasets
  • Evaluate medical AI models across text, image, and audio
  • Supports text, image, and audio evaluation
  • Support multiple healthcare-focused datasets
  • Smart caching for faster re-evaluations
  • Integrate custom models and metrics
  • Registry-based architecture for auto-discovery
  • Multi-modal evaluation capabilities
  • Rich CLI with progress bars and outputs
  • Model-agnostic testing framework
  • Model-agnostic with unified interface
  • Extensible toolkit for researchers
  • Integrated with HuggingFace and local evaluation

Use cases

  • Evaluating medical AI model performance
  • Assess medical AI model performance
  • Testing models on Indian healthcare datasets
  • Test AI models with healthcare datasets
  • Benchmarking text, image, and audio models
  • Integrate custom evaluation metrics
  • Integrating custom evaluation metrics
  • Support multi-modal medical AI research
  • Running efficient batch evaluations

Who is it for

  • Researchers
  • developers
  • healthcare organizations that need to evaluate medical AI systems reliably. It is especially useful for those working in or with the Indian healthcare environment.

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