OussamaEL/MedGemma-4B-ECG
VISIONConcurrency Cost:1Model Size:4.3BQuant:BF16Ctx Length:32kPublished:Jun 25, 2025License:apache-2.0Architecture:Transformer0.0K Open Weights Cold
OussamaEL/MedGemma-4B-ECG is a 4 billion parameter language model fine-tuned from unsloth/medgemma-4b-pt for specialized ECG interpretation. This model excels at generating coherent, human-readable clinical reports from structured ECG findings and patient context. It is designed to synthesize primary ML classifier outputs into detailed impressions, analyses, and clinical recommendations.
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MedGemma-4B-ECG: Specialized Clinical Report Generation
MedGemma-4B-ECG is a 4 billion parameter model developed by OussamaEL, fine-tuned from unsloth/medgemma-4b-pt using the Unsloth library. Its core function is to transform structured ECG findings and patient context, typically from a primary ML classifier, into comprehensive clinical reports.
Key Capabilities
- ECG Report Generation: Synthesizes raw ECG data and patient information into structured clinical reports.
- Detailed Output: Generates impressions, detailed analyses, and clinical recommendations.
- High Accuracy: Achieved a structural correctness score of 1.000 / 1.0 on a hold-out evaluation set.
- Efficient Fine-tuning: Utilized Unsloth + LoRA for memory-optimized training on 500 curated ECG interpretation examples.
Good For
- Automating Clinical Documentation: Ideal for generating preliminary or draft ECG reports in research settings.
- Integrating with ML Classifiers: Designed to work downstream from other machine learning models that identify specific ECG abnormalities.
- Research and Development: Suitable for exploring AI applications in medical diagnostics and report automation, with the caveat that it is not a substitute for professional medical advice.